The Role of the Moisture Conveyor Belt in High Precipitation Events Along the Western Coast of the Philippines During the Southwest Monsoon Season

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Bathan, Lyndon Mark P. Olaguera, Faye Abigail T. Cruz, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7273115/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examines the role of the moisture conveyor belt (MCB) in high precipitation events (HPE) during the southwest monsoon (SWM) season in the Philippines. Using rainfall data from 11 weather stations along the country’s western coast, all high precipitation days (HPDs) from July to September between 1961 and 2022 were identified and categorized into direct tropical cyclone (TC), indirect TC, and SWM HPDs. A relationship between TC distance and intensity with HPD rainfall amount was not found, implying that there are other factors that modulate rainfall. HPEs were identified by grouping together HPDs that were less than 5 days apart. Lag composites of water vapor and surface latent heat fluxes revealed the presence of an MCB over the tropical northern Indian Ocean and the western North Pacific (WNP) during HPEs, but not during non-HPEs. This means that not all TCs to the northeast of the Philippines are able to produce HPEs. The presence of an MCB, which transports moisture from the Indian Ocean towards the Philippines, is integral to the occurrence of HPEs. Also, the Boreal Summer Intraseasonal Oscillation (BSISO) influences MCB formation and HPD occurrence. HPDs usually occur during Phases 5–7 of the BSISO since enhanced convection over the WNP leads to the formation of TCs, while anomalous westerlies over the tropical northern Indian Ocean create the strong monsoon westerlies necessary for MCB formation. Understanding the role of the MCB in the occurrence of HPEs may help improve the forecasting of such events in the Philippines. Enhanced southwest monsoon high precipitation events rainfall variability tropical cyclones Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 Figure 14 Figure 15 1. Introduction The western coast of the Philippines receives around 40–80% of its total annual rainfall during May to September (Olaguera et al., 2022a ). During these months, the western North Pacific summer monsoon (WNPSM), which is a component of the Asian summer monsoon (Matsumoto, 1992; Murakami and Matsumoto, 1994 ), affects the tropical western North Pacific, especially the Philippines. The WNPSM, or the southwest monsoon (SWM) in the Philippine context, is characterized by a series of warm, moist, southwesterly winds, which originate from the Indian Ocean. Due to the warm and moist nature of the winds brought by the SWM, it is usually accompanied by strong convective activity and heavy rainfall, especially when these winds are forced upwards by the mountain ranges near the western coast of Luzon Island (Cayanan et al., 2011 ; Cruz et al., 2013 ). The exact duration of the SWM varies from year-to-year, but studies have estimated the SWM to start as early as mid-May and last as late as mid-November (Kubota et al., 2017 ; Matsumoto et al., 2020 ; Olaguera and Manalo, 2024 ). The peak months of the SWM coincide with the peak months of the typhoon season in the western North Pacific basin. Almost 50% of all the TCs that enter the Philippine Area of Responsibility (PAR) occur during July, August, and September (JAS) (Corporal-Lodangco and Leslie, 2017 ). This results in TCs and the SWM interacting with each other, producing heavy rainfall, especially over the western coast of Luzon Island. Cayanan et al. ( 2011 ) concluded that TCs northeast of Luzon generate stronger southwesterlies over the island of Luzon. These southwesterlies are then orographically lifted as they move over the mountain ranges near the western coast of Luzon, such as the Cordillera Central and Zambales mountain ranges (see Fig. 1 ). This results in intense rainfall over the western coast of Luzon, leading to floods, even when the TC itself did not make landfall in Luzon. The amount of rainfall produced by the interaction of TCs and the SWM has been observed to vary from year-to-year. Bathan et al. ( 2025 ) separated the total rainfall measured over the Philippines into three categories: (1) direct TC rainfall, which is rainfall that directly comes from the TC’s rainbands; (2) indirect TC rainfall, which is rainfall produced by the TC’s enhancement of the SWM; and (3) SWM rainfall, which is rainfall that comes from the SWM alone, without any TCs in the region. Their study discovered that anomalous circulations, which affect the formation and tracks of TCs, are responsible for the interannual variations observed in direct and indirect TC rainfall. Similar variations are also observed in SWM rainfall, which is due to the yearly change in the strength and moisture transport of the SWM. Years with an inherently strong monsoon and anomalous circulations capable of steering TCs towards the northeast of the Philippines may increase the likelihood of extreme rainfall events produced by the TC-enhanced SWM. The occurrence of these extreme rainfall events due to a TC’s enhancement of the SWM has often been studied. Bagtasa ( 2019 ) identified the occurrence of high precipitation events (HPE) over the western coast of the Philippines from 1958 to 2017. The study confirmed the existence of the moisture conveyor belt (MCB) during HPEs, which caused an almost 500% and 700% increase in zonal wind speed and moisture flux over the Philippines, respectively. A subsequent study by Bagtasa ( 2023 ) characterized the 2012 and 2013 enhanced SWM events over Metro Manila. Both events had a TC to the northeast of the Philippines, which enhanced the southwesterly monsoon flow, and a remnant low over the Vietnam-China border, which created low-level westerly jets over the Indochina Peninsula. These conditions led to the formation of the MCB, which transported large amounts of moisture from the Indian Ocean to the WNP and produced intense rainfall over the Philippines. The MCB, also known as an atmospheric river (Guo et al., 2021 ), is defined as a continuous band of high moisture fluxes stretching from the tropical north Indian Ocean to the East Asia region (Kudo et al., 2014 ). Due to the high moisture transport within this band, heavy rainfall can occur where it passes, including the Philippines. Kudo et al. ( 2014 ) stated that the MCB forms due to the lower tropospheric circulations created by a TC. Additionally, their study stated that the presence of monsoon westerlies from the Indian Ocean to the South China Sea is a prerequisite for the formation of the MCB. This implies that not all TCs can produce an MCB, especially if the monsoon westerlies over the Indian Ocean and the South China Sea are anomalously weak. The presence of anomalously strong westerlies over the Indian Ocean and the South China Sea alone also does not equate to an MCB if a TC is not present. Fujiwara et al. ( 2017 ) confirmed this by conducting an experiment that artificially removed a TC from a model run. Their results revealed that the TC-less case was unable to produce an MCB, despite the control case (Typhoon Man-yi, 2007) producing one. Despite the numerous studies regarding HPEs and TC-enhanced SWM cases, the relationship between TC location and intensity with rainfall over the Philippines is still not clear. For instance, Bagtasa ( 2023 ) stated that not all TCs passing through the northeast of the Philippines lead to enhanced SWM rainfall and HPEs. The atmospheric conditions during cases in which TCs did or did not lead to HPEs, even if the locations of these TCs are similar, should be investigated. This study, therefore, aims to (1) examine the relationship between the identified HPEs and TC location/intensity and (2) examine HPEs associated with direct TCs, indirect TCs, and the SWM only. Determining whether TC location and intensity affect the rainfall amount during HPEs may lead to improved forecasting of HPEs during TC-enhanced SWM cases. Furthermore, finding the difference in synoptic conditions between HPEs and TC-related HPEs through composite analysis may provide insights about the driving factors behind the occurrence of HPEs. The rest of the paper is organized as follows: Section 2 enumerates the datasets used in the analysis and presents the methodology for this study. Section 3 examines the relationship between TC location and intensity with rainfall amount of identified HPEs, and the differences in atmospheric conditions between HPEs and non-HPEs. Finally, Section 4 provides the summary and conclusions of the study. 2. Data and Methodology 2.1 Data This study uses rainfall data from 1961 to 2022 for the JAS season, which are the peak months of the SWM season in the Philippines (Lyon and Camargo, 2009 ). The following three datasets are used: Daily rainfall measurements from 11 stations (Ambulong, Baguio City, Coron Island, Cuyo Island, Dagupan City, Iba, Iloilo City, Laoag City, Port Area, Sangley Point, and Science Garden; Fig. 1 ) along the western coast of the Philippines, provided by the Department of Science and Technology-Philippine Atmospheric, Geophysical, and Astronomical Services Administration (DOST-PAGASA). These stations are classified as Type I in the Modified Coronas Climate Classification (Kintanar, 1984 ), where the influence of the southwest monsoon is more pronounced. The 11 stations were specifically chosen since they have no more than 20% missing data during the analysis period; Reanalysis data from the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ERA5). The dataset has a spatial resolution of 0.25° × 0.25° from 1940 to the present (Hersbach et al., 2020 ); TC best track data at six-hour intervals from the Joint Typhoon Warning Center (JTWC), which can be accessed at https://www.metoc.navy.mil/jtwc/jtwc.html?best-tracks . The Boreal Summer Intraseasonal Oscillation (BSISO) Index (Lee et al., 2013 ), which can be accessed at https://cliks.apcc21.org/dataset/bsiso . This data set is available from January 1981 to the present. Only the first mode of the BSISO, with a periodicity of 30–60 days, was used in this study, since this has been found to modulate the occurrence of extreme rainfall events in the Philippines (Olaguera et al., 2022b ). 2.2 Methodology In this study, high precipitation days (HPD) are defined as days wherein the observed rainfall amount for at least one station exceeds its 95th percentile rainfall amount during the JAS season. The identified HPDs are then classified into three categories: direct TC HPDs, indirect TC HPDs, and SWM HPDs. Direct TC HPDs are defined as HPDs with a TC that is within 500 kilometers of any of the stations. Indirect TC HPDs are defined as HPDs with a TC inside the study domain (6.34°N–30°N; 110°E–135°E; see Fig. 1 ), but not within 500 kilometers of any of the stations. SWM HPDs are defined as HPDs without any TCs inside the study domain. The specific distance of 500 kilometers was chosen since several studies have used this to estimate the range of a TC’s direct rainbands (Yokoyama and Takayabu, 2008 ; Lau et al., 2008 ; Nogueira and Keim, 2010 ; Prat and Nelson, 2012; Dare et al., 2012 ; and Khouakhi et al., 2017 ). While some studies, such as Kubota and Wang ( 2009 ), have shown that TC rainfall can reach up to ~ 1000 kilometers away from the TC’s center, this study used a 500-kilometer radius to be more certain that the recorded rainfall is directly from the TC itself. Using a ~ 1000-kilometer radius may inadvertently identify TC-enhanced SWM rain as direct TC rain, especially if the TC is not very large. Additionally, JTWC defines a medium-sized TC as a TC with a radius of 3° to 6°. Assuming a radius of 500 kilometers, which is around 4.5°, puts it very close to the middle of JTWC’s TC size chart (JTWC, 2017 ). To visualize the spatial distribution of the TCs during each HPD, the TC position of each direct TC and indirect TC HPD was plotted. The distance between the center of the TCs and the PAGASA stations was calculated to determine if there is a relationship between the TC distance and the rainfall amount during the HPD. The intensity of the TC during the HPD was used to determine the relationship between TC intensity and the rainfall amount. Sea surface temperature (SST) and specific humidity composites were also created to reveal any correlation between these variables and HPDs. The identified HPDs during the entire analysis period were then grouped into individual high precipitation events (HPEs). An HPE is defined here as a group of HPDs that are not more than five days apart. This ensures that cases with a TC producing consecutive HPDs will only be counted as one HPE. Lag composites of the HPEs were then created to visualize the temporal evolution of the HPE and possibly reveal any precursors to an HPE. The longitudinal range of the composite analysis is from 45°E to 180°E. This allows for the Arabian Sea to be included in the analysis in addition to the Bay of Bengal, South China Sea, and the WNP, which are the regions usually analyzed in studies regarding the MCB (Kudo et al., 2014 ; Fujiwara et al., 2017 ). The inclusion of the Arabian Sea is due to the study by Pérez-Alarcón et al. ( 2023 ), who discovered that the Arabian Sea is also a moisture source for TCs around the vicinity of Taiwan and the northern Philippines. Additionally, only one HPE was chosen per year for the lag composites. Since August usually has the most HPDs (followed by July, then September), the chosen HPE is the first HPE during August. If no HPEs occurred in August of that year, the last HPE during July will be chosen. If there were no HPEs in both July and August, the first HPE during September will be chosen. To confirm whether the presence of the MCB is the main cause of HPEs, composites of both HPEs and non-HPEs were created. Non-HPEs are defined as a group of days wherein none of the stations recorded HPD-level rainfall. Three non-HPEs were chosen for each year of the analysis period. The first non-HPE that was chosen had a TC in roughly the same location as the direct TC HPE. This served as the direct TC non-HPE dataset. The second non-HPE chosen had a TC in roughly the same location as the indirect TC HPE. This served as the indirect TC non-HPE dataset. Finally, the third non-HPE chosen had no TCs in the domain. This served as the SWM non-HPE dataset. To identify the existence and structure of the MCB, lag composite anomalies of water vapor flux and surface latent heat flux were created, following Kudo et al. ( 2014 ) and Fujiwara et al. ( 2017 ) to visualize the extent of the MCB. To test for statistical significance of the composites, a bootstrap t -test (Efron and Tibshirani, 1994 ) was conducted relative to all JAS days during the analysis period. The regular Student’s t -test was repeated 1000 times; each iteration with a different, random sample to obtain the bootstrapped p -value. A bootstrapped p -value of less than 0.05 signifies anomalies that are statistically significant at the 95% confidence level and are, therefore, marked in the composites. Lastly, the moisture flux anomalies over the Arabian Sea, the Bay of Bengal, and the southern half of the South China Sea were analyzed to determine any daily variations, which may increase or decrease the likelihood of an HPE occurring over the Philippines. The extent of these analysis regions are shown in Fig. 2 . The region R1 (10°N–20°N; 55°E–75°E) encompasses the Somali Jet, which is a strong southwesterly wind over the Arabian Sea during the Asian summer monsoon season. The Somali Jet is also believed to precede the onset of the monsoon rainfall over the western coast of India (Halpern and Woiceshyn, 1999 ). R2 (10°N–20°N; 80°E–95°E) encompasses the Bay of Bengal and is dominated by strong westerlies during the Asian summer monsoon season. R3 (5°N–15°N; 105°E–120°E) encompasses the southern half of the South China Sea and features strong winds and high moisture flux values during TC-enhanced SWM cases in the WNP. A time series of the moisture flux anomalies over these three regions for three specific years was created, and the days wherein an HPE occurs over the western coast of the Philippines were marked to see the relationship between these anomalies and HPE occurrence. These regions were chosen as they showed significant anomalies in the lag composite analysis that will be discussed in Section 3 . 3. Results and Discussion 3.1 High precipitation days over the Philippines Out of the 5,642 days in the analysis period, 1,554 days were classified as HPD. Most HPDs occurred in the month of August (605 HPDs), followed by July (522 HPDs), then September (427 HPDs). Direct TC, indirect TC, and SWM HPDs accounted for 15.0%, 32.6%, and 52.4% of all the HPDs, respectively. The breakdown for each type of HPD for every station is shown in Table 1 . Table 1 Number of direct TC, indirect TC, and SWM HPDs for each station during the months of July, August, and September from 1961 to 2022. Values inside parentheses indicate the percentage breakdown of each HPD type for that particular station. The 95th percentile rainfall value for each station is included to show each station’s HPD rainfall threshold. The stations are arranged by latitude with Laoag as the northmost and Iloilo as the southmost in order to easily see the spatial trend. Station Direct TC HPDs Indirect TC HPDs SWM HPDs HPD rainfall threshold (mm) Laoag 82 (28.7) 116 (40.6) 88 (30.8) 80.96 Baguio 72 (25.2) 159 (55.6) 55 (19.2) 110.54 Dagupan 52 (18.2) 138 (48.3) 96 (33.6) 72.19 Iba 34 (11.9) 141 (49.3) 111 (48.8) 117.98 Science Garden 24 (8.5) 127 (45.0) 131 (46.5) 68.30 Port Area 19 (7.1) 121 (45.0) 129 (48.0) 63.33 Sangley Point 19 (8.5) 115 (51.3) 90 (40.2) 63.11 Ambulong 34 (11.9) 130 (45.5) 122 (42.7) 46.37 Coron 11 (4.0) 109 (39.2) 158 (56.8) 67.06 Cuyo 8 (2.9) 77 (28.3) 187 (68.8) 52.00 Iloilo 10 (4.1) 97 (39.9) 136 (56.0) 51.29 There appears to be a clear correlation between the number of direct TC HPDs and the latitude of the station. Stations with a higher latitude have a higher direct TC HPD percentage, while stations with a lower latitude have a lower direct TC HPD percentage. This can be attributed to the nature of TCs during the JAS season when there are fewer TCs landfalling in the Philippines as they move north or northeast of the Philippines (Corporal-Lodangco and Leslie, 2017 ). Meanwhile, the correlation with latitude seems to be a lot less prominent with the indirect TC HPD percentage. Similar to the direct TC HPDs, the three southernmost stations have the lowest indirect TC HPD percentage; however, the station with the fourth lowest indirect TC HPD percentage is the Laoag station, which is the northernmost station. Additionally, unlike the direct TC HPDs, where there was a steep drop in percentage past the Dagupan station, the indirect TC HPD percentages hover around 45% until the Ambulong station, except for the previously mentioned Laoag station. It may be possible that indirect TC HPD percentages depend less on station latitude and more on local topography. SWM rainfall over the Philippines is often produced due to the orographic lifting of the moist monsoon winds by the mountain ranges on the western coast of the country (Cayanan, 2011; Lagmay et al., 2015 ). Cuyo Island is a fairly flat island, with the highest point only having an elevation of 249 meters above sea level. This may explain why the Cuyo station has the lowest indirect TC HPD percentage. Baguio, on the other hand, has an elevation of 1,510 meters. It is also located on the windward side of the Cordillera Central mountain range (Fig. 1 , CC), which may explain why Baguio station has the highest indirect TC HPD percentage. Sangley Point, the station with the second highest indirect TC HPD percentage, is not directly on the windward side of any mountain range. However, Lagmay et al. ( 2015 ) concluded that the volcanoes in the southern region of the Zambales mountain range (Fig. 1 , ZM) can induce heavy rainfall over Greater Metro Manila—which includes the Sangley Point station—despite it not being on the windward side of the volcanoes. Iba, the station with the third highest indirect TC HPD percentage, is on the windward side of the Zambales mountain range (Fig. 1 , ZM). Meanwhile, a possible cause for the fairly low indirect TC HPD percentage for the Laoag station is the position of the mountain range. For the previously mentioned stations with high indirect TC HPD percentages, the high mountains are located directly to the northeast of the station. Since monsoon winds flow from the southwest, this means that these stations are directly on the windward side. For the case of Laoag, the Cordillera Central mountain range (Fig. 1 , CC) does not fully cover the northeastern portion of the station. A small gap is present between the northern coast of Luzon and the northern point of the Cordillera Central mountain range (Fig. 1 , CC). This gap does not have a very high elevation, which may lead to a weaker orographic lifting effect. Finally, the number of SWM HPDs is dependent on the number of TC-related HPDs simply because these are the HPDs that were not classified as direct or indirect TC HPDs. More TC-related HPDs mean fewer SWM HPDs for that specific station and vice versa. Due to the nature of how HPDs are identified, some HPDs featured multiple stations recording HPD-level rainfall. The breakdown of how many individual stations simultaneously recorded HPD-level rainfall for each kind of HPD is shown in Table 2 . HPDs that affect three or more stations at a time can be considered as widespread HPDs. The percentage of widespread HPDs is calculated by summing the percentages in rows 3 through 11 of Table 2 . It is worth noting that the percentages of widespread HPDs for the direct TC, indirect TC, and SWM categories are 42.1%, 28.5%, and 14.8%, respectively. This implies that direct TC cases bring HPD-level rainfall over a larger area compared to indirect TC and SWM cases. This makes sense as TCs have a large rainfall radius, spanning around 500 kilometers from the center of the TC (Yokoyama and Takayabu 2008 ), which leads to heavy TC rainfall over large regions at a time. Meanwhile, SWM HPDs are most likely cases of localized thunderstorms, which only affect one station at a time. This is reflected in the percentage of HPDs where only one station recorded HPD-level rainfall for the SWM category, which is 68.7%. This percentage is significantly higher than the direct TC and indirect TC percentages, which are 36.1% and 50.1%, respectively. The percentages of the indirect TC HPDs in both single-station HPDs and widespread HPDs sit between the direct TC and SWM percentages. TCs in the indirect TC HPDs are usually not close enough to land; therefore, the TCs rainbands do not encompass large areas of the country. Instead, rainfall during indirect TC cases is from the enhancement of the SWM. The enhancement of the SWM forms an MCB, which is a band of high moisture flux that traverses Luzon. This band encompasses a fairly large area of land, which may be the reason why there are more widespread HPDs in the indirect TC category than the SWM category. However, this band usually traverses Luzon at W-E or a SW-NE orientation, which means the length of the band does not cover all the stations at once, since the stations roughly form a N-S oriented line. This may be the reason why there are still more widespread HPDs in the direct TC category than in the indirect TC category. Table 2 Breakdown of how many stations simultaneously experienced HPD-level rainfall during each of the Direct TC, indirect TC, and SWM HPDs during the months of July, August, and September from 1961 to 2022. Values inside parentheses are the percentages of that specific HPD type with anomalies over each region. Number of stations Total HPD Direct TC HPD Indirect TC HPD SWM HPD 1 900 (57.9) 84 (36.1) 256 (50.6) 560 (68.7) 2 291 (18.7) 51 (21.9) 106 (20.9) 134 (16.4) 3 153 (9.8) 41 (17.6) 55 (10.9) 57 (7.0) 4 93 (6.0) 23 (9.9) 36 (7.1) 34 (4.2) 5 55 (3.5) 13 (5.6) 26 (5.1) 16 (2.0) 6 37 (2.4) 14 (6.0) 12 (2.4) 11 (1.3) 7 16 (1.0) 4 (1.7) 9 (1.8) 3 (0.4) 8 8 (0.5) 2 (0.9) 6 (1.2) 0 (0) 9 0 (0) 0 (0) 0 (0) 0 (0) 10 1 (0.1) 1 (0.4) 0 (0) 0 (0) 11 0 (0%) 0 (0) 0 (0) 0 (0) 3.1.1 TC characteristics during HPDs Figure 3 shows the TC positions during all TC-related HPDs in the analysis period. The average coordinates of all direct TCs and indirect TCs were calculated to determine the mean TC positions during direct TC HPDs (yellow “+”) and indirect TC HPDs (yellow “×”). The direct TC mean position is located near the northeastern tip of Luzon Island. This supports Table 1 , wherein the direct TC HPD percentage is higher in the northern stations. The indirect TC mean position is located to the northeast of the Philippines, which is consistent with previous studies that concluded that TCs to the northeast of the Philippines can bring intense rainfall to the western coast of Luzon by enhancing the SWM (Cayanan et al., 2011 ; Bagtasa, 2019 , Bathan et al., 2025 ). It can be observed that the majority of the TC points are located north of the 15°N latitude line. This, again, is due to the nature of the TC tracks during these months. Furthermore, it can also be observed that the majority of the TC points are located to the east of 120°E. This may simply be because there are more TCs that form over the Pacific Ocean compared to the South China Sea, but it may also be due to how the rainbands of the TC and the enhanced SWM interact with each other. A TC east of 120°E causes the enhanced SWM to traverse over Luzon Island. On the other hand, a TC west of 120°E only enhances the SWM over the South China Sea, especially if the TC is much closer to mainland Asia than to the Philippines. The position of each TC during all TC-related HPDs relative to each station is shown in Fig. 4 . This was done to see the differences in TC positions during HPDs for different stations. It can be observed that both the direct and indirect TC mean positions seem to be related to the latitude of the stations. The mean direct and indirect TC positions for Laoag station, the northernmost station, are both between NE and ENE, while the mean direct and indirect TC positions for Iloilo station, the southernmost station, are both between N and NNE. Additionally, the distance between the indirect TC mean position and the station is the greatest for the Iloilo station and the least for the Laoag station. These two observations can simply be because of the TC tracks during this season; therefore, they are much closer and more to the east in stations such as Laoag. The percentage of TCs inside each quadrant (Q1 to Q4, counterclockwise from the northeast quadrant) was calculated and tabulated in Table 3 to determine the general direction of the TC during TC-related HPDs for each station. The table reveals that the majority of the TC-related HPDs occur when a TC is in Q1. The only exception is the direct TC HPDs for Cuyo, in which Q2 is the quadrant with the highest TC percentage. It is worth noting that for the direct TC HPDs, the quadrant with the second highest percentages is generally Q4. Meanwhile, for the indirect TC HPDs, the quadrant with the second highest percentages is generally Q2. This difference in quadrants is due to how direct TCs affect the stations compared to indirect TCs. TCs that directly affect the country usually travel northwestward; therefore, a TC coming directly towards a station, which produces HPD-level rainfall, will appear to the southeast of the station, which is Q4. On the other hand, indirect TCs usually only produce rainfall if the TC is to the north of the station. Cayanan et al. ( 2011 ) discussed a case wherein a TC was still able to produce extreme rainfall despite being to the northwest of Luzon Island, rather than the usual northeast position like with most of the TC-enhanced SWM cases. Additionally, for the direct TC HPDs, the Q2 percentages are lower than the Q4 percentages since there are significantly fewer TCs that form in the South China Sea region and affect the stations from the southwest compared to TCs that form in the Pacific Ocean or the Philippine Sea. For the indirect TC HPDs, the Q4 percentages are lower than the Q2 percentages since indirect TC HPDs are usually TC-enhanced SWM cases. The position of the TC must be to the north of the station for the rain bands of the TC-enhanced SWM to produce rainfall over the stations. Table 3 Percentage of TC points on each quadrant for every TC-related HPD in every station from 1961 to 2022. Q1, Q2, Q3, and Q4, are the NE, NW, SW, and SE quadrants, respectively. Station Direct TC HPDs Indirect TC HPDs Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Laoag 62.2 13.4 0.0 24.4 76.7 6.0 3.4 13.8 Baguio 72.2 19.4 0.0 9.7 88.7 8.2 0.0 3.1 Dagupan 71.2 17.3 0.0 11.5 82.6 11.6 0.7 5.1 Iba 70.6 11.8 0.0 17.6 83.0 14.2 0.0 2.8 Science Garden 70.8 0.0 0.0 29.2 80.3 17.3 0.0 2.4 Port Area 68.4 0.0 0.0 31.6 81.8 17.4 0.0 0.8 Sangley Point 63.2 10.5 0.0 26.3 80.0 15.7 0.0 4.3 Ambulong 73.5 8.8 0.0 17.6 75.4 16.9 0.0 7.7 Coron 54.7 18.1 9.1 18.1 88.1 10.1 0.0 1.8 Cuyo 12.5 75.0 0.0 12.5 71.4 26.0 0.0 2.6 Iloilo 50.0 40.0 0.0 10.0 80.4 19.6 0.0 0.0 Perhaps a more interesting analysis is the relationship between TC distance and intensity with HPD rainfall amount. Figure 5 shows the number of HPDs depending on the distance of the TC from the station, in 100-kilometer increments. The mean rainfall amount for all the HPDs inside each 100-kilometer increment is also shown. The TC distance that produces the most HPDs per station seems to increase in the stations closer to the equator. This may simply be because TCs that produce HPDs are usually located further north. Interestingly, only the first two stations from the north (Laoag and Baguio stations) follow the expected downward trend of mean rainfall amount as the distance between the TC and the station increases, as seen by the negative Pearson correlation coefficients for these stations in Table 4 . For the rest of the stations, it seems that rainfall is not dependent on the TC distance since the correlation coefficients for these stations do not exceed ± 0.5, which is the threshold to be considered as a strong correlation (Turney, 2024 ). Additionally, only the p -values of the first two stations show statistical significance at the 95% confidence level. Table 4 Pearson correlation coefficients for the mean rainfall amount during HPDs and the TC location and intensity. Bold values denote values that are statistically significant at the 95% confidence level. Station HPD mean rainfall and TC distance HPD mean rainfall and TC intensity Laoag -0.75 -0.31 Baguio -0.55 0.18 Dagupan 0.12 -0.01 Iba 0.19 -0.08 Science Garden 0.30 -0.21 Port Area 0.27 -0.27 Sangley Point 0.30 0.14 Ambulong 0.06 -0.05 Coron -0.41 -0.14 Cuyo -0.19 0.06 Iloilo 0.15 0.21 Figure 6 shows the number of HPDs depending on the intensity of the TC, in 5-knot increments. The mean rainfall amount for all the HPDs inside each 5-knot increment is also shown. The mean HPD rainfall amount does not seem to be related to TC intensity. This is supported by Table 4 , which does not indicate a correlation between TC intensity and HPD rainfall amount since none of the stations shows a correlation coefficient that is statistically significant at the 95% confidence level. This is surprising since more intense TCs should produce stronger southwesterlies, which should produce heavier rainfall. This does not seem to be the case, especially since some stations show very subtle decreasing rainfall trends as TC intensity increases (Laoag, Science Garden, Port Area, Sangley Point, and Cuyo Island). This may suggest that there is something else that is modulating enhanced monsoon rainfall over the western coast of Luzon, and not just TC distance and intensity. Additionally, for all the stations, a decreasing trend in the number of HPDs can be observed as TC intensity increases. This is possibly due to the fact that less intense TCs are more common than very intense ones. The HPD rainfall amount not displaying a correlation with TC distance and intensity implies that there must be different factors that directly affect the rainfall amount observed over the Philippines during TC-enhanced SWM events. This may be where the MCB comes in, andits presence might be a significant factor in the occurrence of HPDs. 3.1.2 Sea surface temperature and specific humidity SST and specific humidity composites were also analyzed to reveal any anomalies that might explain the extreme rainfall during HPDs. Figure S1 shows the SST composite anomalies for direct TC, indirect TC, and SST HPDs. For a better comparison, composite anomalies of their respective non-HPDs were also created, as well as their respective differences (HPD minus non-HPD). All TC-related cases, whether HPD or non-HPD, exhibit SST cooling in the waters surrounding the Philippines. Studies by Wang et al. ( 2005 ) and Kubota et al. ( 2016 ) stated that summer monsoon rainfall over Luzon has a negative correlation with the surrounding SSTs, which can be attributed to the rainfall produced by the monsoon cooling the affected waters. This cooling effect by rainfall is not just limited to monsoon rainfall, but also occurs with TC rainfall, which may explain the large patches of negative SST anomalies around the Philippines in the direct TC composites (Figures S1 a and S1d) and to the northeast of the Philippines in the indirect TC composites (Figures S1 b and S1e) since these regions coincide with the usual location of the TC during these cases. SST cooling can also be observed outside the extent of the TC’s rainbands. For example, the cooling observed in the South China Sea in all the TC-related composites cannot simply be attributed to TC rainfall, as the TC is not located over this region. The cooling observed in this region, as well as in the tropical northern Indian Ocean for the indirect TC cases, may be due to the increased rainfall, greater cloud cover, and stronger winds of the enhanced SWM. Additionally, the SST cooling observed in these regions is greater in magnitude during HPDs compared to non-HPDs, which can be clearly seen in the difference plots of the TC-related cases (Figures S1 c and S1f). This is expected as there is more rainfall produced by the enhanced SWM during HPDs compared to non-HPDs. This cooling pattern over the tropical Indian Ocean is consistent with the findings of Hegde et al. ( 2016 ). The study stated that higher SSTs in the region may weaken the structure of the MCB, whereas lower SSTs may strengthen it. This SST-MCB relationship was also validated in the SST experiments of Fujiwara et al. ( 2017 ). A more structured MCB may lead to more rainfall over the Philippines, which explains the lower SSTs in the tropical Indian Ocean during HPDs. For SWM HPDs, despite not having a TC, cases of monsoon surges may produce SWM HPDs and subsequently cool the waters in the South China Sea. The opposite can be seen in the SWM non-HPD composite (Figure S1 h), which shows positive SST anomalies in almost the entire tropical region. This heating may be due to the lack of rainfall, cloud cover, and winds due to the suppression of the monsoon over these regions. Another interesting finding is the significant heating of the ocean waters north of 30°N during both indirect TC composites and the SWM HPD composite. Bathan et al. ( 2025 ) discovered a similar SST pattern over this region during years with high and low SWM rainfall over the Philippines. The SSTs around this region seemed to be higher during the years with high SWM rainfall compared to the years with low SWM rainfall. Additionally, Liu et al. ( 2023 ) also had similar findings, wherein SSTs around Japan were higher in strong monsoon years compared to weak monsoon years. In the case of HPDs, SWM HPDs may occur more often during strong monsoon years, while SWM non-HPDs occur more often during weak monsoon years. This may explain the similar SST pattern. However, the exact reason for this SST pattern is unknown and may need further investigation. Figure S2 is a similar set of composites to Figure S1 , but for vertically integrated specific humidity. The main takeaway in these composites is the significantly higher moisture content around the TC, over the Philippines, the South China Sea, and the Indochina Peninsula during the HPD composites. The higher moisture content signified by the high specific humidity values may be directly related to the higher rainfall amount experienced during HPDs. However, the structure of the MCB is not fully captured by vertically integrated specific humidity alone. 3.2 Comparing HPEs and non-HPEs Bagtasa ( 2023 ) emphasized that the establishment of the MCB is necessary for the occurrence of extreme rainfall during TC-enhanced SWM events. Therefore, with the use of lag composites, this section will attempt to determine if the presence of the MCB is related to the occurrence of HPDs. First, the identified HPDs were grouped into separate HPEs. HPDs that are within 5 days of each other were counted as one HPE. This is to ensure that weather systems that last for multiple days and produce consecutive HPDs, such as TCs, are only counted as one event. This grouping resulted in 139 direct TC HPEs, 230 indirect TC HPEs, and 375 SWM HPEs. On average, a direct TC HPE lasted 1.88 days, an indirect TC HPE lasted 2.79 days, and an SWM HPE lasted 2.94 days. The short duration of direct TC HPEs is due to the average translational speed of a TC, which is 19 kilometers per hour (PAGASA, 2024 ). With this translational speed, it would take a TC 2.19 days to cross the 1000-kilometer diameter circle around a station. Since most TCs do not cross the circle at its widest point, the actual duration is shorter than 2.19 days. Indirect TC HPEs have a longer duration than direct TC HPEs since the region where a TC can enhance the SWM is significantly larger than the areas of the circles around a station. For example, a TC can track northeast of the Philippines for several days before making landfall in Taiwan or eastern China. During these days, the TC can produce heavy rainfall over Luzon by enhancing the SWM. This is exactly what happened with Typhoon Haikui in 2012, which brought heavy rainfall over the Philippines from August 6–10 (Bagtasa, 2023 ). Lastly, SWM HPEs lasting the longest may be the result of monsoon surges, which bring strong rainfall over one station at a time. As the monsoon surge evolves, a different station may experience strong rainfall, but since these HPDs may be less than five days apart, they are counted as one HPE. Cayanan et al. ( 2011 ) discussed a heavy rainfall event without a TC that lasted for several days, from August 17–21, 2006. For the HPE lag composites, the first day of the HPE is assigned as lag 0. The preceding days are lag − 5 to -1, while the succeeding days are lag + 1 to + 5. For the non-HPE lag composites, lag 0 in TC-related non-HPEs is the day when the TC position is similar to that of the TC position during the lag 0 composite of the direct or indirect TC HPEs. Lag 0 in SWM non-HPEs is the first day of a group of days that did not record HPD-level rainfall. This group of days is composed of at least five consecutive non-HPDs. Composites of vertically integrated water vapor flux and surface latent heat flux were used in this section to possibly identify the presence of the MCB, since these are the variables that can depict the structure of the MCB (Kudo et al., 2014 ; Fujiwara et al., 2017 ). 3.2.1 Direct TC cases The composite anomalies from lag − 5 to lag 0 for the direct TC cases are shown in Fig. 7 . The direct TC HPE composites (Fig. 7 a to 9 f) and the direct TC non-HPE composites (Fig. 7 g to 7 l) both show the evolution of a TC, depicted as the large circular region of very high positive moisture flux anomalies, which originates in the Philippine Sea, travels west-northwestwards, and crosses the northern tip of Luzon. The location of the TC for each time step is similar for both the HPE and non-HPE composites, which means that the selection of the direct TC non-HPEs was effective. A noticeable difference in the TC structure between the HPE and non-HPE composites is the size of the region of high positive moisture flux anomalies. The region of the moisture flux anomalies within the TC is noticeably larger for the HPE composites compared to the non-HPE composites. This makes sense as TCs with more moisture may bring more rainfall, which then causes the HPEs. The most striking difference between the two kinds of composites is the presence of positive anomalies over some regions of the northern Indian Ocean. For each time step in the HPE composites, a long band of positive moisture flux anomalies can be seen stretching from the Arabian Sea to the TC. These anomalies are present well before the TC appears in the lag composites, as seen in the lag − 5 and − 4 in the HPE composites (Figs. 7 a and 7 b). This implies that the presence of TC is not necessarily the cause of these anomalies. During this time, the positive moisture flux anomalies are mostly over the Arabian Sea and the Bay of Bengal, as well as over the Indian subcontinent. As the TC’s structure develops (Figs. 7 c, 7 d, and 7 e), the positive anomalies between 100°E and 120°E start to intensify and connect the moisture flux anomalies over the northern Indian Ocean to the anomalies of the TC. Finally, at lag 0 (Fig. 7 f), nearly the entire Philippines is covered by the high positive moisture flux anomalies from the MCB and the TC. On the other hand, in the non-HPE composites, the Arabian Sea, Indian subcontinent, Bay of Bengal, and Indochina Peninsula barely show positive moisture flux anomalies in any of the lag composites. Only during the lag − 1 and lag 0 composites can a very small region of positive moisture flux anomalies be observed over the southern half of the South China Sea. This may imply a local enhancement of the SWM due to the TC, but not nearly as intense and as widespread as the ones seen in the HPE composites. Next, the composite anomalies from lag 0 to lag + 5 for the direct TC cases are shown in Fig. 8 . Much like Fig. 7 , the TC locations in the direct TC HPE composites (Fig. 8 a to 8 f) and the direct TC non-HPE composites (Fig. 8 g to 8 l) are very similar. After affecting northern Luzon, the TC continues to travel west-northwestward until it makes landfall over southern China. The HPE composites show positive moisture flux anomalies over the Arabian Sea and the Bay of Bengal even after the TC and the MCB dissipate in the lag + 5 composite. On the other hand, the non-HPE composites do not show a single trace of the MCB aside from very small and localized anomalies over some regions in the northern Indian Ocean. For the HPE composites, it is worth noting that the positive moisture flux anomalies move away from the Philippines starting lag + 3. This is due to the position of the TC at this point in time. The TC is far enough west of the Philippines that the moisture from the enhanced SWM does not reach the Philippines anymore, as it wraps around the rear flank of the TC instead of continuing northwestwards. These composites also show an enhancement in the East Asian monsoon (EAM) from lag 0 to lag + 4 of the HPE case (Fig. 8 a to 8 e). Positive moisture flux anomalies are present to the northeast of the TC, affecting the Korean Peninsula and some parts of Japan. These anomalies complete the entire structure of the MCB. This shows the large-scale moisture transport from the Indian Ocean to Japan and the Korean Peninsula via the MCB, as stated by Kudo et al. ( 2014 ). The presence of the positive anomalies throughout the entire region, even after the TC dissipates, may imply that these positive moisture flux anomalies over the northern Indian Ocean last at least 10 days. However, it is still unclear whether or not the passing of the TC lengthened the duration of these anomalies. For the non-HPE composites, the dissipation of the TC seems to be quicker than that of the HPE TC. This may be due to non-HPE direct TCs being weaker in general. For the surface turbulent latent heat flux, composite anomalies from lag − 5 to lag 0 for the direct TC cases are shown in Figure S3 . Similar to the direct TC moisture flux anomalies, the direct TC HPE composites (Figure S3 a to S3f) and the direct TC non-HPE composites (Figure S3 g to S3l) of the surface latent heat flux both show the evolution of a TC, depicted as the large circular region of positive surface latent heat flux anomalies. Similar to the direct TC moisture flux composites, the size of the anomalies representing the TC seems to be smaller in the non-HPE composites compared to the HPE composites, which further suggests that direct TCs during non-HPEs are less intense or hold less moisture compared to direct TCs during HPEs. In the HPE composites, positive surface latent heat flux anomalies can be observed over the Arabian Sea and the Bay of Bengal for all time steps. There also seems to be small positive surface latent heat flux anomalies over the southern half of the South China Sea, which increase in magnitude once the TC starts to become more organized (Figures S3 d, S3e, and S3f). In the non-HPE composites, only minimal patches of positive anomalies can be observed over the tropical northern Indian Ocean. The development of the TC (Figures S3 j, S3k, and S3l) still does not produce any significant positive anomalies outside the WNP, implying that the MCB during this case simply does not exist. The composite anomalies from lag 0 to lag + 5 for the direct TC cases are shown in Figure S4 . The TC locations in the direct TC HPE composites (Figure S4 a to S4f) and the direct TC non-HPE composites (Figure S4 g to S4l) are, once again, very similar. The HPE composites continue to show the entire structure of the MCB as a long band of positive surface latent heat flux anomalies even after the TC dissipates in the lag + 5 composite, which, again, suggests that these anomalies are independent of the TC. On the other hand, the non-HPE composites do not feature any surface latent heat flux anomalies aside from the positive anomalies present over the South China Sea as the TC tracks to the northwest of the Philippines. This implies that, while there is some kind of enhancement of the SWM due to the TC, the enhancement is confined within the WNP and does not have the same spatial extent as a fully developed MCB. 3.2.2 Indirect TC cases Next, the composite anomalies from lag − 5 to lag 0 for the indirect TC cases are shown in Fig. 9 . The indirect TC HPE composites (Fig. 9 a to 9 f) and the indirect TC non-HPE composites (Fig. 9 g to 9 l) show very similar TC locations throughout all the time steps, which means that the selection of the indirect TC non-HPEs was effective. The TC during the indirect TC case forms slightly more to the east and deeper into the Pacific Ocean compared to the direct TC case. The TC then takes on a northwesterly track and moves towards the east of Taiwan, unlike in a direct TC case, wherein the TC passes over the northern tip of Luzon. Similar to the direct TC composites, the structure of the MCB in the indirect TC HPE composites is significantly more defined compared to the non-HPE composites, although the non-HPE composites do show a band resembling the MCB, albeit weaker. Additionally, the TC itself seems to have higher anomalies and encompasses a larger region in the non-HPE composites compared to the HPE composites, which may either suggest that TCs in the non-HPE composites are less intense than the TCs in the HPE composites or that TCs do not need to be of high intensity to produce HPEs. Just like in the direct TC HPE composites, the MCB in the indirect TC HPE composites appears before the TC forms (Figs. 9 a and 9 b). During this time, very strong positive anomalies are present over the Arabian Sea, the Indian subcontinent, and the Bay of Bengal. These anomalies also stretch across the Indochina Peninsula and towards the South China Sea, but are significantly weaker, especially past 100°E. As the TC starts to become organized during lag − 3 and − 2 (Figs. 9 c and 9 d), the moisture flux anomalies extend over the South China Sea due to the TC’s enhancement of the SWM. By lag − 1 and 0 (Figs. 9 e and 9 f), the MCB fully forms as the moisture flux anomalies extend towards the TC. At this point, high positive moisture flux values can be seen over the southern tip of the Indochina Peninsula, the southern half of the South China Sea, and the central portion of the Philippines. In the non-HPE composites, the spatial characteristics of the anomalous band of moisture flux are similar to those of MCB as seen in the HPE composites, but are significantly weaker, especially around the South China Sea region in the lag − 1 and 0 composites (Figs. 9 k and 9 l). The composite anomalies from lag 0 to lag + 5 for the indirect TC cases are shown in Fig. 10 . The indirect TC HPE composites (Fig. 10 a to 10 f) and the indirect TC non-HPE composites (Fig. 10 g to 10 l) show the same TC track progression. The TC continues its northwestward trajectory and makes landfall over the northern tip of Taiwan and eventually in eastern China. For the HPE composites, the Philippines experiences its highest moisture flux anomalies during lag + 1 and lag + 2. This may imply that these are the peak days of indirect TC HPEs. Unlike those in the direct TC HPE composites, wherein the enhanced SWM stops affecting the Philippines by lag + 3, the enhanced SWM in the indirect TC HPE case continues to affect the Philippines even up until lag + 5. In the lag + 4 composites for both cases (Figs. 10 e and 10 k), similar to the direct TC HPE composites, positive moisture flux anomalies appear to the northeast of the dissipating TC. These anomalies imply an enhancement in the EAM; however, the area of enhancement is slightly different between the HPE and non-HPE cases. In the HPE case, the enhanced EAM is over the Korean Peninsula; in the non-HPE case, the enhanced EAM is over Japan. Additionally, the enhanced EAM during the HPE case completes the entire structure of the MCB, once again showing the large-scale moisture transport from the Indian Ocean to the Korean Peninsula via the MCB. From lag + 3 to lag + 5 of the HPE composites, the MCB continues to linger over the South China Sea and the Philippines even after the TC starts to dissipate, which indicates that these areas may continue to experience rainfall. This may be the reason why indirect TC HPEs last slightly longer than direct TC HPEs. For the non-HPE composites, the moisture flux anomalies over the Philippines in lag + 1 and lag + 2 are not as high as the ones in the HPE composites. By lag + 3, only the northwestern coasts of the Philippines are affected by the enhanced SWM. By lag + 4 and lag + 5, the Philippines is covered by subtle negative moisture flux anomalies, which are the total opposite of the lag + 4 and lag + 5 of the HPE composites. This implies that the rainfall in the non-HPE cases lasts shorter than in the HPE cases. The surface latent heat flux composite anomalies from lag − 5 to lag 0 for the indirect TC cases are shown in Figure S5 . The indirect TC HPE composites (Figure S5 a to S5f) and the indirect TC non-HPE composites (Figure S5 g to S5l) both show the evolution of the TC from lag − 2 and onwards. The TC in the non-HPE composites seems to have higher surface latent heat flux anomalies compared to the TC in the HPE composites, similar to the indirect TC moisture flux composites (Fig. 9 ), which further suggests that the TC in the non-HPE composite is more intense. A region of anomalies that resembles the MCB can be seen in both the HPE and the non-HPE composites as the band of positive surface latent heat flux anomalies over the Arabian Sea, the Indian subcontinent, the Bay of Bengal, the southern regions of the Indochina Peninsula, and the southern half of the South China Sea. The difference is that the anomalies in the aforementioned regions are significantly higher in the HPE composites compared to the non-HPE composites, suggesting a much more developed MCB during HPEs. The composite anomalies from lag 0 to lag + 5 for the indirect TC cases are shown in Figure S6 . The indirect TC HPE composites (Figure S6 a to S6f) and the indirect TC non-HPE composites (Figure S6 g to S6l) both show the TC continuing its northwestward trajectory and eventually landfalling over eastern China. The MCB in the HPE composites shows higher surface latent heat flux anomalies throughout all the time steps compared to the non-HPE composites, implying that the MCB is more defined during HPEs. The anomalies in the HPE composites also stay even after the TC has dissipated, unlike in the non-HPE composites, wherein the anomalies weaken and almost disappear, especially in the lag + 4 and + 5 composites (Figures S6 k and S6l). 3.2.3 SWM cases The composite anomalies from lag − 5 to lag 0 for the SWM (no TC) cases are shown in Fig. 11 . In the HPE composites (Fig. 11 a to 11 f), a band of positive moisture flux anomalies similar to the anomalies seen in the previous HPE composites can be observed over the Arabian Sea, the Indian subcontinent, and the Bay of Bengal in the composites preceding lag 0. A region of positive moisture flux anomalies can also be found to the northeast of the Philippines, at around 20°N and 130°E in the lag − 5 composite. This anomaly travels northwestward and eventually ends up on the coast of eastern China in the lag − 3 and − 2 composites. A region of negative moisture flux anomalies also starts to form to the north of the Philippines in the lag − 2 composite, which gradually grows in size. By lag 0, this region of negative moisture flux anomalies encompasses a large region of the South China Sea, the tip of northern Philippines, and the entirety of Taiwan. The lack of positive anomalies and the presence of a large region of negative anomalies near the Philippines is expected, as the SWM HPE itself only occurs from lag 0 onwards. In the non-HPE composites (Fig. 11 g to 11 l), a similar band of positive anomalies can be observed over the Arabian Sea, the Indian subcontinent, and the Bay of Bengal in the composites preceding lag 0. The largest difference in the non-HPE composites compared to the HPE composites, however, is the presence of positive anomalies to the north of the Philippines from lag − 5 to lag − 1. This is simply due to the fact that the days leading up to the non-HPE are actually HPDs since the non-HPE only starts at lag 0. This transition can first be noticed in the lag − 2 composite, where a region of negative anomalies starts to form to the east of the Philippines, at around 20°N and 140°E. This region of negative anomalies gradually strengthens in magnitude and grows in size while slowly migrating westward. By lag 0, this region of negative anomalies is now situated to the northeast of the Philippines. Next, the composite anomalies from lag 0 to lag + 5 for the SWM cases are shown in Fig. 12 . In the HPE composites (Fig. 12 a to 12 f), the large region of negative anomalies to the north of the Philippines slowly starts migrating northward. At the same time, the positive anomalies present over the Bay of Bengal and the Indochina Peninsula extend to the east, covering regions of the South China Sea and the southern half of the Philippines. By lag + 2, the positive anomalies over the South China Sea have increased in magnitude and point more northwestward, towards Luzon Island. From lag + 3 to lag + 5, the positive anomalies seem to affect the entire western coast of Luzon Island. The large region of negative anomalies mentioned earlier has also disappeared in these time steps. In the non-HPE composites (Fig. 12 g to 12 l), the region of the negative anomalies to the northeast of the Philippines continues to grow in size. More importantly, the negative anomalies seem to extend southwestward, towards the southern half of the South China Sea. From lag + 2 to lag + 5, almost the entire Philippines is covered by significant negative anomalies, which implies noticeably suppressed rainfall over the entire country during these days. Aside from the anomalies over the Philippines and the South China Sea, there seems to be no strong anomalies over the northern Indian Ocean region. This may suggest that the suppression of the SWM during SWM non-HPE cases is only localized to the WNP region. The surface latent heat flux composite anomalies from lag − 5 to lag 0 for the SWM cases are shown in Figure S7 . In the HPE composites (Figure S7 a to S7f), positive surface latent heat flux anomalies can be observed over the Arabian Sea starting at the lag − 5 composite, which slightly weakens in the following time steps. Small positive anomalies can also be found over the South China Sea and the Philippines, which weaken until the lag − 1 composite, probably due to the formation of a large area of negative surface latent heat flux anomalies centered over the island of Taiwan. This area of negative anomalies reaches its peak around lag 0 (Figure S7 f). In the non-HPE composites (Figure S7 g to S7l), positive anomalies are present over the Philippines until the lag − 1 composite. The positive anomalies are slowly replaced by a large region of negative anomalies coming from the east of the Philippines. Finally, the composite anomalies from lag 0 to lag + 5 are shown in Figure S8 . In the HPE composites (Figure S8 a to S8f), the large region of negative anomalies to the north of the Philippines slowly starts to disappear at the same time as more positive anomalies start to affect the country. In the lag + 1 composite, only the southern half of the Philippines is affected by positive surface latent heat flux anomalies. By the lag + 3 composite, the positive anomalies now affect the entire western coast of Luzon. This northward migration of these anomalies during the days succeeding lag 0 can also be observed in the moisture flux anomalies as seen in Fig. 12 . In the non-HPE composites (Figure S8 g to S8l), the large region of negative anomalies to the east of the Philippines slowly migrated westward. By the lag + 2 composite, the entire Philippines is covered in negative anomalies, as well as the southern half of the South China Sea. Similar to the moisture flux anomalies for this case, the suppression of the surface latent heat flux seems to only be localized over the WNP region and does not reach the Bay of Bengal and the Arabian Sea. The composites presented in this section have provided evidence that the MCB is a very important factor in the occurrence of HPEs. The formation of the MCB during the presence of TCs also seems to be reliant on the moisture flux and surface latent heat flux anomalies in certain regions of the northern Indian Ocean, mainly the Arabian Sea and the Bay of Bengal. Whenever these regions experience high anomalies, TCs that pass near the Philippines enhance the SWM, which creates an MCB that extends from the Arabian Sea to the TC. The MCB traverses several regions of land, such as the Indian subcontinent, the Malay Peninsula, the Indochina Peninsula, and the Philippines, possibly producing heavy rainfall over these areas. During cases wherein the Arabian Sea and the Bay of Bengal do not feature strong positive anomalies, a TC may still be able to enhance the SWM. However, based on the presented composites, the moisture content of the enhanced SWM is significantly lower due to the lack of an efficient moisture transport from the Indian Ocean. Additionally, this enhancement is only localized to the WNP, mainly the South China Sea and the Philippines. This leads to less rainfall over the Philippines. 3.3 The MCB and the influence of the BSISO It can be noticed from the previous section that there are certain regions that experience higher vertically integrated moisture flux and surface latent heat flux anomalies during HPEs compared to non-HPEs. Specifically, these regions are the Arabian Sea, the Bay of Bengal, and the southern half of the South China Sea. Moisture flux and surface latent heat flux anomalies can be found over the Arabian Sea and Bay of Bengal before and after the passage of the TC in the WNP, implying that these anomalies are independent of the TC. Another observation from the composites is that the anomalies over the southern half of the South China Sea appear once a TC approaches the Philippines. These anomalies, however, are significantly stronger during HPEs, when there are also positive anomalies over the Arabian Sea and the Bay of Bengal, compared to non-HPEs. The formation of the MCB, which leads to the HPEs over the Philippines, seems to be dependent on the moisture flux and surface latent heat flux over the Arabian Sea, the Bay of Bengal, and the southern half of the South China Sea. This section will attempt to determine the daily moisture flux fluctuations in these regions and its effect on the occurrence of HPEs over the Philippines. 3.3.1 The MCB region The moisture flux anomalies over these three regions, which will now be referred to as R1, R2, and R3 (as seen in Fig. 2 ), were averaged to obtain a singular daily value for each region. To determine if the anomalies over these three regions are related to the occurrence of HPDs over the Philippines, the HPDs that simultaneously feature positive moisture flux anomalies in each region were determined. The results are shown in Table 5 . For both direct and indirect TC HPDs, R3 has the highest percentages, with 71.7% of direct TC HPDs and 78.3% of indirect TC HPDs coinciding with days that have positive moisture flux anomalies over R3. This is expected as this region is the closest to the WNP; therefore, the closest to the TCs, and more prone to experiencing positive anomalies whenever a TC passes nearby. The percentage differences between R1 and R2 during direct and indirect TC HPDs are within one percent of each other, possibly implying that the anomalies in R1 and R2 both have a somewhat equal relationship with HPDs over the Philippines. Interestingly, the trend of the percentages is flipped for the SWM HPDs. Here, 62.8% of SWM HPDs coincided with days that have positive moisture flux anomalies over R1. This is higher than the R2 and R3 percentages, which are 59.8% and 58.2%, respectively. The percentages for the SWM HPDs may suggest that these kinds of HPDs are related to the overall strength of the Asian monsoon system as a whole, instead of just the SWM over the South China Sea and the Philippines. This is in line with the SWM HPE composites in Fig. 11 , wherein the moisture flux anomalies start from the northern Indian Ocean prior to the HPE. These anomalies then extend over the South China Sea and the Philippines during the HPE. Table 5 Number of direct TC, indirect TC, and SWM HPDs that simultaneously featured positive moisture flux anomalies over R1, R2, or R3. Values inside parentheses are the percentages of that specific HPD type with anomalies over each region. Type of HPD Days with positive R1 anomalies Days with positive R2 anomalies Days with positive R3 anomalies Direct TC 121 (51.9) 123 (52.8) 167 (71.7) Indirect TC 309 (61.1) 308 (60.9) 396 (78.3) SWM 512 (62.8) 487 (59.8) 474 (58.2) Total 942 (60.6) 918 (59.1) 1037 (66.7) Next, the correlation between the number of days that feature positive moisture flux anomalies over each of the three regions and the number of HPDs experienced by the Philippines every year was calculated. The Pearson correlation coefficients for HPDs and R1, R2, and R3 positive anomaly days are 0.82, 0.81, and 0.87, respectively. All three values show a very strong positive correlation and are all statistically significant at the 95% confidence level. The very high R3 correlation coefficient is expected, as this is the region closest to the Philippines; therefore, the more days with positive anomalies in this region, the more HPDs the Philippines experiences. An interesting finding is that the correlation coefficients for R1 and R2 are nearly as high as R3, despite being further away from the Philippines. This suggests that all three regions are interconnected and the occurrence of HPDs in the Philippines is not only due to the local enhancement of the SWM, but is also due to a large-scale strengthening of the entire Asian monsoon. For a deeper look into the daily moisture flux variations, the years 2005, 2012, and 2021 were selected, and a time series of the daily moisture flux anomalies for all three regions was created. All three of these years had TCs that enhanced the SWM and brought heavy rainfall over the western coast of the Philippines. For the year 2005, it was Typhoon Matsa. For the year 2012, the TCs were Saola and Haikui. For the year 2021, the TCs were In-fa and Cempaka. The moisture flux anomaly time series for each of the three years is presented in Fig. 13 . Included in the figure are the TC days, depicted as the black stars, as well as the HPDs, depicted as the colored dots. Red dots represent direct TC HPDs, blue dots represent indirect TC HPDs, while yellow dots indicate SWM HPDs. At first glance, the moisture flux anomalies in all three regions are observed to drastically vary in magnitude throughout the season. Episodes of positive and negative moisture flux anomalies, which last for several weeks, can be seen in all three years for all three regions. R1, which is the Arabian Sea region, is the furthest away from the WNP. Because of this, it can be assumed that TCs in the WNP are unlikely to have a large effect on the anomalies over this region. This means that the peaks and valleys in the moisture flux anomalies of R1 are most likely not related to WNP TC activity. However, anomalies in R2 and R3 do somewhat follow the overall trend of the R1 anomalies. Table 6 shows the Pearson correlation coefficients for R1 and R2, R2 and R3, and R1 and R3. It is no surprise that the regions that are adjacent to each other (R1 and R2, R2 and R3) have a higher Pearson correlation coefficient than the regions that are far apart (R1 and R3). Although, aside from the year 2012, the Pearson correlation coefficient for R1 and R3 is still statistically significant at the 95% confidence level. While the moisture flux anomalies of all three regions seem to be somewhat related, it seems that another factor that modulates the moisture flux anomalies over R3 is the presence of TCs in the WNP. This behavior is seen as the high, localized spikes in the moisture flux anomalies in R3 during the presence of TCs. For the 2005 case (Fig. 13 a), spikes in R3 anomalies can be observed during the TC days of early August, as well as during the TC days of mid and late September. For the 2012 case (Fig. 13 b), spikes in R3 anomalies can be observed during the TC days of late July, early August, mid-September, and late September. For the 2021 case (Fig. 13 c), spikes in R3 anomalies can be observed during the TC days of mid-July, early August, and early September. This makes sense as R3 is the region closest to the WNP. However, the positive anomalies in R3 whenever a TC is near the Philippines seem to be greater in magnitude if R1 and R2 are also experiencing positive anomalies. For example, in the year 2005 (Fig. 13 a), all three regions experienced an increase in moisture flux anomalies during the second half of July. After this spike of anomalies, the R2 and R3 anomalies eventually decreased. R1 followed this decreasing trend at the start of August; however, the presence of Typhoon Matsa and Tropical Storm Sanvu caused the R3 anomalies to spike once again, which produced three consecutive indirect TC HPDs. Once the two TCs disappeared, the R3 anomalies quickly dropped to the level of the R1 and R2 anomalies, which have been steadily decreasing since the start of August. Anomalies over all three regions continue to drop until late August. The presence of Typhoon Talim during the final days of August caused an uptick in the moisture flux values; however, these anomalies are fairly low and were only able to produce one HPD since R1 and R2 were experiencing negative anomalies. R1 and R2 anomalies peaked once again in mid-September, leading to sudden peaks in R3 anomalies during the presence of TCs. In the 2012 graph (Fig. 13 b), the 2012 heavy rainfall event can be seen as the consecutive TC days and HPDs spanning from late July until early August. Compared to 2005, R1 during this year didn’t experience any major anomalies until mid-September. However, it is worth noting that R1 and R2 displayed positive anomalies during the 2012 heavy rainfall event. The presence of Typhoons Saola and Haikui caused high peaks in R3 anomalies, producing several HPDs. Further into the season, two TCs (Kai-tak and Tembin) appeared during mid-late September, producing several consecutive TC days. However, these TC days did not produce as many HPDs compared to Saola and Haikui. This may be due to the dip in R1 and R2 anomalies during this time, which also caused the R3 anomalies to dip. The presence of Typhoon Sanba during mid-September was able to cause a spike in R3 anomalies; however, this spike was lower compared to the spikes seen with Saola and Haikui, which may be due to the low anomalies found in R1 and R2. In the 2021 graph (Fig. 13 c), a very large increase in moisture flux anomalies can be observed in R1 during mid-July. This spike in anomalies seems to be independent of TC activity or the anomalies in R2 and R3. However, this increase in R1 anomalies did allow R2 and R3 to significantly increase during the lifespan of Typhoon In-fa, producing several consecutive HPDs. The anomalies in all three regions suddenly dropped in early August and continued to display negative anomalies throughout the rest of the month. The R1 anomalies slowly increased until early September, allowing R2 and R3 to significantly increase in the presence of Tropical Storm Conson. Table 6 Pearson correlation coefficients for the moisture flux anomalies over the regions R1 and R2, R2 and R3, and R1 and R3 for the years 2005, 2012, and 2021. Bold values denote values that are statistically significant at the 95% confidence level. Year R1 and R2 R2 and R3 R1 and R3 2005 0.797 0.554 0.504 2012 0.603 0.488 0.148 2021 0.634 0.739 0.628 It can be observed from Fig. 13 that the fluctuations in moisture flux in all three regions are seemingly in phase with one another. Furthermore, due to the distance between R1 and the WNP, anomalies over R1 seem to be independent of TC activity over the WNP. This may suggest that any moisture flux fluctuations experienced over the Arabian Sea are due to a completely different mechanism. TC activity does seem to affect the anomalies over R3, and to a lesser extent, R2. Local peaks in R3 anomalies, and sometimes R2 anomalies, seem to coincide with the existence of TCs over the WNP. This means that TCs can locally enhance the SWM over the South China Sea. However, the magnitude of these local peaks is still dependent on the overall trend of the moisture flux anomalies of all three regions. For example, if all three regions show positive moisture flux anomalies and a TC passes through the northeast of the Philippines, the chances of an HPD, especially an indirect TC HPD, occurring are higher than if all three regions show negative moisture flux anomalies. This is in line with Bagtasa ( 2023 ), wherein it was stated that not all TCs to the northeast of the Philippines produce heavy rainfall over the western coast of Luzon. The conditions in all three regions must be right for a TC to produce a fully-formed MCB. 3.3.2 The influence of the BSISO A possible explanation for the moisture flux variations during HPEs may be the BSISO (Lee et al., 2013 ; Guo et al., 2021 ). From the three years discussed in this section, days that featured a moisture flux anomaly value exceeding 200 kg m − 1 s − 1 averaged over the three regions were identified. Out of the 35 identified days, 26 occurred during either the 5th, 6th, or 7th phase of the BSISO. Due to the propagation characteristics of the BSISO, enhanced convection is present over the Philippines during these phases, which may be the reason for the extreme rainfall. The remaining nine days were identified to occur during phase 8 of the BSISO, wherein the area of enhanced convection is situated to the northeast of the Philippines (Lee et al., 2013 ; Kikuchi, 2021 ). For a deeper analysis, the moisture flux anomalies over all three regions were averaged for each BSISO phase from 1981 to 2022 (BSISO index only goes back to 1981) and is shown in Figure S9. Interestingly enough, the moisture flux anomalies for all three regions appear to peak during phase 6 despite the BSISO convection only being near the vicinity of R3 during this phase. The positive outgoing longwave radiation (OLR) anomalies observed over the tropical northern Indian Ocean during phase 6, as reported by Lee et al. ( 2013 ) and Kikuchi ( 2021 ), imply suppressed convection over regions R1 and R2. Similarly, during phases 2 and 3, the BSISO convection is situated over the tropical northern Indian Ocean, yet R1 and R2 experience negative moisture flux anomalies. This discrepancy may imply that the moisture flux and OLR anomalies accompanying the convective region of the BSISO are not directly related. This may explain why the moisture flux anomalies in all three regions are seemingly in phase with one another (observed in Fig. 13 ), as the moisture flux anomalies do not follow the eastward propagation pattern of the BSISO. Instead, the moisture flux anomalies over the three regions seem to be dependent on the horizontal wind anomalies that accompany the BSISO convection. For example, during phases 2 and 3, wherein the BSISO convection is over the tropical northern Indian Ocean, easterly horizontal wind anomalies are present over R1 and R2, which suppresses the westerly monsoon flow and moisture transport. Similarly, during phases 6 and 7, the convection is over the Philippines, but westerly horizontal wind anomalies are present in all three regions. This enhances the westerly monsoon flow and the moisture transport. To further investigate the relationship between HPDs and the BSISO, all the direct TC, indirect TC, and SWM HPDs were plotted into their respective phase space diagrams and are shown in Fig. 14 . For this section, significant BSISO HPDs are defined as HPDs with a BSISO amplitude greater than 1. The phase with the highest percentage of significant BSISO HPDs actually differs depending on the type of HPD. For direct TC HPDs, the peak is during phase 5. For indirect TC HPDs, the peak is during phase 7. Finally, for SWM HPDs, the peaks are phases 5 and 7; however, the percentages for phases 4 and 6 are not that far behind. This implies that the occurrence of SWM HPDs is more spread out during phases 4 to 6, unlike in direct and indirect TC HPDs, where there are evident peaks. The difference in peaks between direct and indirect TC HPDs may be attributed to the changes in the horizontal wind anomalies and the location of the BSISO convection during each phase. Moon et al. ( 2018 ) and Zhang et al. ( 2023 ) stated that TC genesis points typically follow the BSISO convection when it’s moving along the WNP. A similar phenomenon was observed by Kikuchi and Wang ( 2010 ), but with cyclones over the northern Indian Ocean. The cause for this is the midtropospheric vertical motion accompanying the BSISO convection. This vertical motion is the biggest factor controlling TC genesis in the region (Moon et al., 2018 ). During phase 5 of the BSISO, the area of convection is situated over the South China Sea and the Philippines; therefore, any TCs forming in this region will most likely be classified as a direct TC due to their proximity to the Philippines. On the other hand, during phase 7 of the BSISO, the area of convection is now situated more to the east of the Philippines. This leads to TCs forming much deeper into the Pacific Ocean compared to TCs that formed during phase 5. Additionally, Wu et al. ( 2023 ) discovered changes in the location of the 5870 geopotential height at 500 hPa during the different BSISO phases. The 5870 gpm contour line typically marks the edge of the western North Pacific subtropical high (WNPSH) and has a significant impact on the tracks of TCs. During phase 5, a significant westward extension in the 5870 gpm contour line occurs, causing the WNPSH to possibly steer TCs towards the Philippines, leading to more direct TC HPDs. During phase 7, there is still a westward extension, but not as drastic compared to phase 5. This may allow some TCs during this phase to recurve northward instead of traversing the Philippines, possibly leading to more indirect TC HPDs. Overall, the BSISO seems to have an effect on the MCB since it modulates the two key ingredients for MCB formation: the strong monsoon westerlies over the tropical northern Indian Ocean and South China Sea, and the occurrence of TCs over the WNP. Guo et al. ( 2021 ) examined the relationship between the occurrence and distribution of MCB and the BSISO. They found more frequent formation of the MCB over the WNP during the northward propagation of the convective envelope of the BSISO to the subtropics, particularly between phases 7 and 8. Significant positive moisture flux anomalies over R1, R2, and R3 can be observed during phases 5, 6, and 7, which imply the presence of strong monsoon westerlies, which is the first ingredient for the MCB (Figure S9). At the same time, TC formation is more likely in these phases since the BSISO convection, which creates instability and vertical motion anomalies, is now situated over the WNP. With the presence of the strong monsoon westerlies and the TC, the MCB will now most likely form and cause extreme rainfall in various regions, especially over the western coast of the Philippines. 4. Summary and Conclusions The HPDs from July to September for the period 1961 to 2022 were identified and separated into direct TC HPDs, indirect TC HPDs, and SWM HPDs, depending on the location of the TC present. Direct TC, indirect TC, and SWM HPDs account for 15.0%, 32.6%, and 52.4% of all HPDs in the analysis period, respectively. The average distribution of the HPDs within a JAS season is as follows: 33.6% for July, 39.9% for August, and 27.5% for September. This distribution closely resembles the evolution of the SWM over the Philippines. The SWM fully establishes itself during July, reaches its peak during August, and gradually weakens in September. For direct TC HPDs, a correlation between direct TC HPD occurrence and the latitude of the station was found. This is simply because TCs during the JAS season tend to move north or northeast of the Philippines, thus affecting the northern stations more. For indirect TC HPDs, the occurrence seems to be related to the local geography surrounding the station, such as high mountains to the east and northeast of the station. This is due to the orographic lifting effect, which forces the warm, moist southwesterlies brought by the TC-enhanced SWM upwards, leading to heavy rainfall. For SWM HPDs, their occurrence is simply dependent on the occurrence of the TC-related HPDs. More TC-related HPDs generally mean fewer SWM HPDs. The mean TC position during direct TC and indirect TC HPDs was identified. The mean direct TC position is over Cagayan province, which is located on the northern edge of Luzon Island. This is expected since JAS TCs affect the northern Philippines more. The mean indirect TC position is situated several hundred kilometers northeast of Luzon. This TC position is in line with findings by Cayanan et al. ( 2011 ) and Bagtasa ( 2019 ), which stated that the western coast of Luzon experiences heavy rainfall when a TC to the northeast is present. For both direct and indirect TC HPDs, the majority of the TCs that produce HPDs are situated in the NE quadrant of the station. This is expected since for both direct and indirect TC HPDs, a northeast TC position means that the winds experienced by the station come from the southwest. Southwesterly winds can produce more rainfall via the orographic lifting effect since high mountains are present to the east and northeast of most of the stations. However, the quadrant with the second highest HPD percentage is different for both direct and indirect TC HPDs. The quadrant with the second highest HPD percentage for the direct TC HPDs is the SE quadrant. This is because TCs usually travel northwestward, which means that TCs that will directly pass a station will appear on that station’s SE quadrant. Meanwhile, the quadrant with the second highest HPD percentage for the indirect TC HPDs is the NE quadrant. This follows the study by Cayanan et al. ( 2011 ), which stated that TCs to the northwest of Luzon Island are still able to bring extreme rainfall over the western coast of Luzon. Determining a relationship between TC distance and rainfall amount during HPDs was attempted, but a clear correlation between the two was not found. The same can be said for TC intensity and rainfall amount. This suggests that the characteristics of the TC, such as position and intensity, are not the only factors in the modulation of the rainfall over the western coast of Luzon during enhanced SWM cases. This was verified in the lag composites comparing HPEs and non-HPEs. The HPE composites featured very noticeable moisture flux and surface latent heat flux anomalies over the Arabian Sea, the Bay of Bengal, and the southern half of the South China Sea, which were not present or not as evident in the non-HPE composites. The anomalies over the Arabian Sea and the Bay of Bengal also seem to be present before and after the lifespan of the TC, suggesting that these anomalies are independent of the TC. A schematic diagram was created based on the results of the lag composites (Fig. 15 ). The region of moisture flux anomalies over the tropical northern Indian Ocean and the WNP, as seen by the blue shaded areas, is significantly larger during the TC-related HPEs compared to the TC-related non-HPEs. This implies that the MCB is only present, or more pronounced, during HPEs. Additionally, SWM HPEs also feature a long band of positive moisture flux over the tropical northern Indian Ocean and the South China Sea, similar to that of the MCB. This band grazes the western coast of the Philippines, causing heavy rainfall over these regions during SWM HPEs. For the SWM non-HPEs, a large region of negative moisture flux anomalies covers the entirety of the Philippines, the Philippine Sea, and the southern half of the South China Sea. The lower moisture content of the SWM during these days is responsible for the suppressed rainfall over the western coast of the Philippines. The moisture flux anomalies over the Arabian Sea, the Bay of Bengal, and the southern half of the South China Sea, during three specific years, were analyzed to find a relationship between the anomalies and the occurrence of TC-related HPEs. The time series revealed that all three regions featured daily variations in moisture flux anomalies. The anomalies in the three regions seem to be in phase with one another as most of the time, all three regions either showed positive or negative anomalies. It was also observed that the R1 anomalies are somewhat independent of TC activity over the WNP, while R3 is noticeably dependent. Spikes in moisture flux anomalies over R3 can be observed during TC days; however, these spikes still follow the overall trend of R1 and R2. This means that spikes in moisture flux in R3 when R1 and R2 exhibit positive anomalies are more likely to cause HPDs than when R1 and R2 exhibit negative anomalies. Lastly, the role of the BSISO in the formation of the MCB was investigated. It was determined that the horizontal wind anomalies accompanying the propagation of the BSISO convection are able to modulate the moisture flux over R1, R2, and R3. The moisture flux over these regions seems to peak during phase 6 when the BSISO convection is over the Philippines. Additionally, TCs are more likely to form during phases 5 to 7 as the BSISO convection creates a region over the WNP of very favorable conditions for TC genesis. The higher frequency of TCs and the strong moisture flux over the northern Indian Ocean and the South China Sea can produce the MCB, which causes HPDs/HPEs. Kudo et al. ( 2014 ) and Fujiwara et al. ( 2017 ) only defined the MCB starting from the Bay of Bengal, stretching over the Indochina Peninsula, and then over the WNP and the Philippines. Future studies regarding the MCB could include the Arabian Sea, as the composites presented in this study revealed that the moisture flux anomalies of the MCB also extend to this region. Further studies regarding the BSISO could also possibly include moisture flux composites in addition to the typical horizontal wind and OLR composites. The evolution of moisture transport during the life cycle of the BSISO may provide further insights into rainfall patterns and help deepen our understanding of monsoon behavior during each BSISO phase. Declarations Competing Interests The authors declare that they have no competing interests. Declaration of Interests The authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper. Author Contribution All authors contributed to the study conception and design. Implementation of the methodology and formal analysis were done by A.A.L. Bathan, L.M.P. Olaguera, F.A.T. Cruz, J.R.T. Villarin, J.T. Maquiling, M.O.L. Cambaliza, J.A. Manalo, and J. Matsumoto supervised, reviewed, and edited the manuscript. The first draft of the manuscript was written by A.A.L. Bathan, L.M.P. Olaguera. All authors commented on previous versions of the manuscript, read, and approved the final manuscript. Acknowledgement A.A.L. Bathan, L.M.P. Olaguera, F.A.T. Cruz, and J.R.T. Villarin were supported by the High-definition Clean Energy, Climate, and Weather Forecasts for the Philippines project of the Manila Observatory. Part of this study was supported by Grant-in-Aid for Scientific Research 22H04938; PI Kei Yoshimura of the University of Tokyo and No. 24K00172; PI Yoshiyuki Kajikawa of Kobe University. Data Availability The rainfall data set from PAGASA may be requested through the following link: https://www.pagasa.dost.gov.ph/climate/climate-data . The reanalysis data from ERA5 are publicly available at https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview . References Bagtasa G (2019) Enhancement of Summer Monsoon Rainfall by Tropical Cyclones in Northwestern Philippines. J Meteor Soc Japan. 97: 967−976. https://doi.org/10.2151/jmsj.2019-052 Bagtasa G (2023) Characterization of the 2012 and 2013 Metro Manila "Enhanced Habagat" Heavy Rainfall Events. Philipp J Sci. 152: 123–135. https://doi.org/10.56899/152.S1.10 Bathan AAL, Olaguera LMP, Cruz FAT, Villarin JRT, Maquiling JT, Cambaliza MOL, Manalo JA, Matsumoto J (2025) Influence of tropical cyclones, southwest monsoon, and tropical cyclone-enhanced southwest monsoon on rainfall variability over the western coast of the Philippines. Atmos Res. 326. https://doi.org/10.1016/j.atmosres.2025.108273 Cayanan E, Chen T, Argete J, Yen M, Nilo P (2011) The effect of tropical cyclones on southwest monsoon rainfall in the Philippines. J Meteor Soc Japan. 89A, 123–139. https://doi.org/10.2151/jmsj.2011-A08 Corporal-Lodangco I, Leslie L (2017) Climatology of Philippine tropical cyclone activity: 1945-2011. Int J Climatol. 37: 3525–3539. https://doi.org/10.1002/joc.4931 Cruz F, Narisma G, Villafuerte II M, Chua KU, Olaguera LM (2013) A climatological analysis of the southwest monsoon rainfall in the Philippines. Atmos Res. 122:609–616. https://doi.org/10.1016/j.atmosres.2012.06.010. Dare R, Davidson N, Mcbride J (2012) Tropical cyclone contribution to rainfall over Australia. Mon Wea Rev. 140: 3606–3619. https://doi.org/10.1175/MWR-D-11-00340.1 Das PK (1986) Monsoons. Fifth WMO Lecture. World Meteorological Organization. Geneva Efron B, Tibshirani RJ (1994) An Introduction to the Bootstrap. Philadelphia, PA: Chapman & Hall/CRC Press. 436 pp Fujiwara K, Kawamura R, Hirata H, Kawano T, Kato M, Shinoda T (2017) A positive feedback process between tropical cyclone intensity and the moisture conveyor belt assessed with Lagrangian diagnostics. J Geophys Res Atmos. 122:12,502–12,521. https://doi.org/10.1002/ 2017JD027557 Guo X, Zhao N, Kikuchi K, Nasuno T, Nakano M, Annamalai H (2021) Atmospheric rivers over the Indo-Pacific and its associations with the boreal summer intraseasonal oscillation. J Clim. 34: 9711–9728. https://doi.org/10.1175/JCLI-D-21-0290.1 Halpern D, Woiceshyn PM (1999) Onset of the Somali Jet in the Arabian Sea during June 1997. J Geophys Res. 104(C8):18041–18046. https://doi.org/10.1029/1999JC900141 Hegde AK, Kawamura R, Kawano T (2016) Evidence for the significant role of sea surface temperature distributions over remote tropical oceans in tropical cyclone intensity. Clim. Dyn. 47: 623–635. https://doi.org/10.1007/s00382-015-2859-.8 Hersbach H, Bell B, Berrisford P, Hirahara S, Horányi A, Muñoz‐Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A (2020) The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146:1999–2049. https://doi.org/10.1002/qj.3803 JTWC (2017) Frequently Asked Questions. Joint Typhoon Warning Center. https://www.metoc.navy.mil/jtwc/jtwc.html?faq. Accessed 02 April 2025 Khouakhi A, Villarini G, Vecchi GA (2017) Contribution of tropical cyclones to rainfall at the global scale. J Clim. 30:359–372. https://doi.org/10.1175/JCLI-D-16-0298.1 Kintanar RL (1984) Climate of the Philippines. Quezon City, Philippines: Philippine Atmospheric, Geophysical and Astronomical Services Administration. Kikuchi K (2021) The Boreal Summer Intraseasonal Oscillation (BSISO): A Review. J Meteor Soc Japan. 99. https://doi.org/10.2151/jmsj.2021-045 Kikuchi K, Wang B (2010) Formation of tropical cyclones in the Northern Indian Ocean associated with two types of tropical intraseasonal oscillation modes. J Meteor Soc Jpn. 88: 475–496. https://doi.org/10.2151/jmsj.2010-313 Kubota H, Kosaka Y, Xie S-P (2016) A 117-year long index of the Pacific-Japan pattern with application to interdecadal variability. Int J Climatol. 36: 1575–1589. https://doi.org/10.1002/joc.4441. Kubota H, Shirooka R, Matsumoto J, Cayanan E, Hilario F (2017) Tropical cyclone influence on the long-term variability of Philippine summer monsoon onset. Prog Earth Planet Sci. 4, 27. https://doi.org/10.1186/s40645-017-0138-5. Kubota H, Wang B (2009) How much do tropical cyclones affect seasonal and interannual rainfall variability over the Western North Pacific? J Clim. 22: 5495–5510. https://doi.org/10.1175/2009JCLI2646.1 Kudo T, Kawamura R, Hirata H, Ichiyanagi K, Tanoue M, Yoshimura K (2014) Large-scale vapor transport of remotely evaporated seawater by a Rossby wave response to typhoon forcing during the Baiu/Meiyu season as revealed by the JRA-55 reanalysis. J Geophys Res Atmos. 119: 8825–8838. https://doi.org/10.1002/2014JD021999 Lagmay AM, Bagtasa G, Crisologo I, Racoma BA, David C (2015) Volcanoes magnify Metro Manila's southwest monsoon rains and lethal floods. Front Earth Sci. 2. https://doi.org/10.3389/feart.2014.00036 Lau KM, Zhou YP, Wu HT (2008) Have tropical cyclones been feeding more extreme rainfall? J Geophys Res Atmos. 113: D23113. https://doi.org/10.1029/2008JD00996 Lee JY, Wang B, Wheeler MC, Fu X, Waliser D, Kang (2013) Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Clim Dyn. 40: 493–509. https://doi.org/10.1007/s00382-012-1544-4 Liu S, Xu J, Tu S, Zheng M, Chen Z (2023) Relationship between South China Sea Summer Monsoon and Western North Pacific Tropical Cyclones Linkages with the Interaction of Indo-Pacific Pattern. Atmosphere. 14: 645. https://doi.org/10.3390/atmos14040645 Lyon B, Camargo S (2009) The seasonally-varying influence of ENSO on rainfall and tropical cyclone activity in the Philippines. Clim Dyn. 32: 125–141. https://doi.org/10.1007/s00382-008-0380-z. Matsumoto J, Olaguera LM, Nguyen–Le D, Kubota H, Villafuerte II M (2020) Climatological seasonal changes of wind and rainfall in the Philippines. Int J Climatol. 40. https://doi.org/10.1002/joc.6492. Moon J, Wang B, Lee S, Ha K (2018) An intraseasonal Genesis Potential Index for tropical cyclones during northern hemisphere summer. J Clim. 31: 9055–9071. https://doi.org/10.1175/JCLI-D-18-0515.1 Murakami T, Matsumoto J (1994) Summer monsoon over the Asian Continent and western North Pacific. J Meteor Soc Jpn. 72: 719–745. https://doi.org/10.2151/jmsj1965.72.5_719. Nogueira RC, Keim BD (2010) Annual volume and area variations in tropical cyclone rainfall over the Eastern United States. J Clim. 23: 4363–4374, https://doi.org/10.1175/2010JCLI3443.1 Olaguera LMP, Cruz F, Dado JM, Villarin JR (2022a) Complexities of extreme rainfall in the Philippines. In: Unnikrishnan, A., Tangang, F., Durrheim, R.J. (eds) Extreme Natural Events. Springer, Singapore. https://doi.org/10.1007/978-981-19-2511-5_5 Olaguera LMP, Manalo JA, Matsumoto J (2022b) Influence of boreal summer intraseasonal oscillation on rainfall extremes in the Philippines. Int J Climatol. 42: 4656–4668. Olaguera LM, Manalo J (2024) Objective determination of the withdrawal dates of the southwest monsoon (Habagat) season of the Philippines using cumulative rainfall. Philipp J Sci. 153(5):1579–1591. PAGASA (2018) Climate of the Philippines. Philippine Atmospheric, Geophysical and Astronomical Services Administration. https://www.pagasa.dost.gov.ph/information/climate-philippines. Accessed 25 March 2025 PAGASA (2024) Climate of the Philippines. Philippine Atmospheric, Geophysical and Astronomical Services Administration. https://www.pagasa.dost.gov.ph/information/about-tropical-cyclone. Accessed 13 March 2025 Pérez-Alarcón A, Sorí R, Fernández-Alvarez JC, Nieto R, Gimeno L (2023) Moisture source for the precipitation of tropical cyclones over the Pacific Ocean through a Lagrangian approach. J Clim. 36: 1059–1083. https://doi.org/10.1175/JCLI-D-22-0287.1 Prat OP, Nelson BR, (2013) Mapping the world's tropical cyclone rainfall contribution over land using the TRMM Multi-satellite Precipitation Analysis. Water Resour Res. 49: 7236–7254. https://doi.org/:10.1002/wrcr.20527 Turney S (2024) Pearson Correlation Coefficient (r) | Guide & Examples. Scribbr. https://www.scribbr.com/statistics/pearson-correlation-coefficient/. Accessed 14 April 2025 Wang B, Ding Q, Fu X, Kang I, Jin K, Shukla J, Doblas-Reyes F (2005) Fundamental challenge in simulation and prediction of summer monsoon rainfall, Geophys Res Lett. 32, L15711. https://doi.org/10.1029/2005GL022734. Wu P, Ling Z, He H (2023) The effects of intraseasonal oscillations on landfalling tropical cyclones in the Philippines during the boreal summer. Front Earth Sci. 11:1106291. https://doi.org/10.3389/feart.2023.1106291 Yokoyama C, Takayabu YN (2008) A Statistical Study on Rain Characteristics of Tropical Cyclones Using TRMM Satellite Data. Mon Weather Rev. 136: 3848–3862. https://doi.org/10.1175/2008MWR2408.1 Zhang S, Zhao H, Klotzbach PJ, Jiang X, Chen G, Chen S (2023) Interannual Variability in the Boreal Summer Intraseasonal Oscillation Modulates the Meridional Migration of Western North Pacific Tropical Cyclone Genesis. J Clim. 36(13): 4543–4558. https://doi.org/10.1175/JCLI-D-22-0406.1 Additional Declarations No competing interests reported. Supplementary Files SupplementaryFigures.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7273115","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":508097636,"identity":"3141a6f1-7c4d-4ac2-af57-769903f937cb","order_by":0,"name":"Alwin Andriel L. Bathan","email":"","orcid":"","institution":"Ateneo de Manila University","correspondingAuthor":false,"prefix":"","firstName":"Alwin","middleName":"Andriel L.","lastName":"Bathan","suffix":""},{"id":508097637,"identity":"c0f0b804-afb0-413b-97e6-2f540bbdbf13","order_by":1,"name":"Lyndon Mark P. Olaguera","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYBACPgYGxgMQJvMBZhQpAxxa2IAYqoUtAaqFmWgtPAZEahE7fODAj1+H5czbz3x8XPDHJt/g+PmDjyvbGPLMcWmRTks42Nt32FjmTO5m45ltaZYbziQzG55tYyi2bMClJcfgAG/P4cQZDLnbpHkbDhtINiSzSTa2MSRuOIBby8G/PYfrZ/C/eSbN8+e/gWT/Y8JaDvP8OJwgIZHDJs3DdsCAX4KgLWkJh2Ub0g1nSDwzBvolGajlsbFhwzmJYgMcWvilkw8+fPPHWl6CP/khMMTsDNj4Ex8+bCizycOlBQwY2zBE2CQS8GgAgj9YRAhoGQWjYBSMghEEAHTQXNz6rA3JAAAAAElFTkSuQmCC","orcid":"","institution":"Ateneo de Manila University","correspondingAuthor":true,"prefix":"","firstName":"Lyndon","middleName":"Mark P.","lastName":"Olaguera","suffix":""},{"id":508097638,"identity":"65e9e2df-6c5f-4098-ae1f-d028af0d8cdd","order_by":2,"name":"Faye Abigail T. Cruz","email":"","orcid":"","institution":"Manila Observatory","correspondingAuthor":false,"prefix":"","firstName":"Faye","middleName":"Abigail T.","lastName":"Cruz","suffix":""},{"id":508097639,"identity":"9c2b48d3-1326-479a-bf0c-1e6375ff5ae7","order_by":3,"name":"Jose Ramon T. Villarin","email":"","orcid":"","institution":"Manila Observatory","correspondingAuthor":false,"prefix":"","firstName":"Jose","middleName":"Ramon T.","lastName":"Villarin","suffix":""},{"id":508097640,"identity":"77538167-58d4-4ce9-9616-37e99c87f313","order_by":4,"name":"Joel T. Maquiling","email":"","orcid":"","institution":"Ateneo de Manila University","correspondingAuthor":false,"prefix":"","firstName":"Joel","middleName":"T.","lastName":"Maquiling","suffix":""},{"id":508097641,"identity":"6b5261fa-7630-47cd-94a7-f84ca53b2a3a","order_by":5,"name":"Maria Obiminda L. Cambaliza","email":"","orcid":"","institution":"Ateneo de Manila University","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"Obiminda L.","lastName":"Cambaliza","suffix":""},{"id":508097642,"identity":"d693f1f4-6a3a-4cec-b8c6-42b331fa9a58","order_by":6,"name":"John A. Manalo","email":"","orcid":"","institution":"Department of Science and Technology, Philippine Atmospheric, Geophysical and Astronomical Services Administration","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"A.","lastName":"Manalo","suffix":""},{"id":508097643,"identity":"321e10b9-91be-43f2-9136-f6eea310f4b1","order_by":7,"name":"Jun Matsumoto","email":"","orcid":"","institution":"Tokyo Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Jun","middleName":"","lastName":"Matsumoto","suffix":""}],"badges":[],"createdAt":"2025-08-01 16:38:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7273115/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7273115/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90288691,"identity":"a7022d5d-21dd-493c-afaa-e04db8f2cade","added_by":"auto","created_at":"2025-09-01 07:00:25","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":394196,"visible":true,"origin":"","legend":"\u003cp\u003eThe black box represents the domain of the study. The colored dots indicate the locations of the 11 PAGASA stations. Enclosed in the small boxes are the mountain ranges that induce rainfall over some of the stations, which are: the Cordillera Central mountain range (CC), the Zambales mountain range (ZM), and the Southern Sierra Madre mountain range (SSM). The topography (shades) is from the U.S. Geological Survey (GMTED2010). Dataset can be accessed at https://www.temis.nl/data/gmted2010/\u003c/p\u003e","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/dba3db909403539ce251d13e.jpg"},{"id":90288702,"identity":"c7c4cd9c-a291-4a7c-8de2-860e035c68f8","added_by":"auto","created_at":"2025-09-01 07:00:26","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":410584,"visible":true,"origin":"","legend":"\u003cp\u003eThe three regions used in the intraseasonal analysis: R1 (Arabian Sea); R2 (Bay of Bengal); and R3 (southern half of the South China Sea). The topography (shades) is from the U.S. Geological Survey (GMTED2010). Dataset can be accessed at https://www.temis.nl/data/gmted2010/\u003c/p\u003e","description":"","filename":"fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/b14bbfbd2a621ccfae29611a.jpg"},{"id":90288701,"identity":"6e7c7b0e-373b-431e-bb56-d5999cc74d44","added_by":"auto","created_at":"2025-09-01 07:00:26","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":662631,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of all the TCs during each direct TC and indirect TC HPD. Red circles indicate the direct TCs while blue circles indicate the indirect TCs. The yellow “+” is the mean direct TC location while the yellow “×” is the mean indirect TC location. The black box is the domain of the study. The total number of direct TC and indirect TC HPDs during the analysis period is also shown.\u003c/p\u003e","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/28397498e63ed5c5b3f1ff0d.jpg"},{"id":90289171,"identity":"7c5708b2-1bf7-48d4-840b-e826063bbc1c","added_by":"auto","created_at":"2025-09-01 07:08:25","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":622277,"visible":true,"origin":"","legend":"\u003cp\u003eLocations of all the TCs during each direct TC and indirect TC HPD for each individual station. The red circles indicate the direct TCs while the blue circles indicate the indirect TCs. The yellow “+” is the mean direct TC location while the yellow “×” is the mean indirect TC location. The concentric circles indicate the distance away from the station, and are spaced out at 500-kilometer intervals.\u003c/p\u003e","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/8f3290b665cc99a90e5272f6.jpg"},{"id":90288683,"identity":"942ef8d6-505f-4bf9-b203-68e2a3efd766","added_by":"auto","created_at":"2025-09-01 07:00:25","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":751205,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between TC distance from the station and the HPD rainfall amount measured by the station. The red triangles are the mean rainfall amounts for the TCs inside each 100-kilometer wide bin. The gray bars depict the number of HPDs for each bin.\u003c/p\u003e","description":"","filename":"fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/a28c8c18829459811d4c6eac.jpg"},{"id":90288707,"identity":"c13d9442-d8a7-4299-b5d2-36624f8d5152","added_by":"auto","created_at":"2025-09-01 07:00:26","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":746702,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between TC intensity and the HPD rainfall amount measured by the station. The red triangles are the mean rainfall amounts for the TCs inside each 5-knot wide bin. The gray bars depict the number of HPDs for each bin.\u003c/p\u003e","description":"","filename":"fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/79cafb9c6197dc34d0fecac3.jpg"},{"id":90288666,"identity":"c9f36f34-abe2-41ca-ac14-05af1a70a235","added_by":"auto","created_at":"2025-09-01 07:00:24","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1053206,"visible":true,"origin":"","legend":"\u003cp\u003eVertically integrated water vapor flux magnitude (kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; shades) and direction (500 kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; vector) anomalies for the days preceding the first day of the HPE during direct TC HPEs (a-f) and direct TC non-HPEs (g-l). The hatched regions are anomalies that are statistically significant at the 95 % confidence level.\u003c/p\u003e","description":"","filename":"fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/0094ef3d82d0dea49727c0b1.jpg"},{"id":90289181,"identity":"060c63dc-8593-4601-a5d6-f4e1ffc53225","added_by":"auto","created_at":"2025-09-01 07:08:26","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1027203,"visible":true,"origin":"","legend":"\u003cp\u003eVertically integrated water vapor flux magnitude (kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; shades) and direction (500 kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; vector) anomalies for the days succeeding the first day of the HPE during direct TC HPEs (a-f) and direct TC non-HPEs (g-l). The hatched regions are anomalies that are statistically significant at the 95 % confidence level.\u003c/p\u003e","description":"","filename":"fig8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/d8ed1e0f1ff2d106b3128dd6.jpg"},{"id":90288695,"identity":"137eb02b-b9db-4b5e-bfa5-b04a597d36de","added_by":"auto","created_at":"2025-09-01 07:00:25","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":1118716,"visible":true,"origin":"","legend":"\u003cp\u003eVertically integrated water vapor flux magnitude (kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; shades) and direction (500 kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; vector) anomalies for the days preceding the first day of the HPE during indirect TC HPEs (a-f) and indirect TC non-HPEs (g-l). The hatched regions are anomalies that are statistically significant at the 95 % confidence level.\u003c/p\u003e","description":"","filename":"fig9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/a7c4254b78c85f476b76eaca.jpg"},{"id":90288690,"identity":"a2e60f4c-7c60-4732-983d-98f44c226523","added_by":"auto","created_at":"2025-09-01 07:00:25","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":1068765,"visible":true,"origin":"","legend":"\u003cp\u003eVertically integrated water vapor flux magnitude (kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; shades) and direction (500 kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; vector) anomalies for the days succeeding the first day of the HPE during indirect TC HPEs (a-f) and indirect TC non-HPEs (g-l). The hatched regions are anomalies that are statistically significant at the 95 % confidence level.\u003c/p\u003e","description":"","filename":"fig10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/cdd841adf62ebae5cd838775.jpg"},{"id":90288668,"identity":"d65d540a-f7f3-4570-a5d4-906d4c0e6259","added_by":"auto","created_at":"2025-09-01 07:00:24","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":979616,"visible":true,"origin":"","legend":"\u003cp\u003eVertically integrated water vapor flux magnitude (kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; shades) and direction (500 kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; vector) anomalies for the days preceding the first day of the HPE during SWM HPEs (a-f) and SWM non-HPEs (g-l). The hatched regions are anomalies that are statistically significant at the 95 % confidence level.\u003c/p\u003e","description":"","filename":"fig11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/b9adc1d2fa697ded48bcf572.jpg"},{"id":90288671,"identity":"43c56e5a-c81d-4556-8d6b-d4aa2b100202","added_by":"auto","created_at":"2025-09-01 07:00:24","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":980058,"visible":true,"origin":"","legend":"\u003cp\u003eVertically integrated water vapor flux magnitude (kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; shades) and direction (500 kg m\u003csup\u003e-1\u003c/sup\u003es\u003csup\u003e-1\u003c/sup\u003e; vector) anomalies for the days succeeding the first day of the HPE during SWM HPEs (a-f) and SWM non-HPEs (g-l).\u0026nbsp; The hatched regions are anomalies that are statistically significant at the 95 % confidence level. The hatched regions are anomalies that are statistically significant at the 95 % confidence level.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e","description":"","filename":"fig12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/8aab7bdd999614a344fd5b21.jpg"},{"id":90289184,"identity":"a739edcc-17c8-47b7-82b1-2de56d8464ee","added_by":"auto","created_at":"2025-09-01 07:08:26","extension":"jpg","order_by":13,"title":"Figure 13","display":"","copyAsset":false,"role":"figure","size":561256,"visible":true,"origin":"","legend":"\u003cp\u003eTime series of the moisture flux anomalies over R1, R2, and R3 during the years 2005 (a), 2012 (b), and 2021 (c). The black stars indicate days wherein a TC is inside the study domain. Red dots indicate direct TC HPDs. Blue dots indicate indirect TC HPDs. Yellow dots indicate SWM HPDs.\u003c/p\u003e","description":"","filename":"fig13.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/9d1a3cebfabea318e6defa47.jpg"},{"id":90288670,"identity":"1018ae8b-b224-4a0b-a87c-3ae01c5eb986","added_by":"auto","created_at":"2025-09-01 07:00:24","extension":"jpg","order_by":14,"title":"Figure 14","display":"","copyAsset":false,"role":"figure","size":448469,"visible":true,"origin":"","legend":"\u003cp\u003eBSISO phase space diagrams for direct TC (a), indirect TC (b), and SWM HPDs (c). Each dot corresponds to a HPD. The percentage of significant MJO (amplitude \u0026gt; 1) HPDs for each phase is also shown.\u003c/p\u003e","description":"","filename":"fig14.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/f6daf545842a69736e2e5cfe.jpg"},{"id":90288706,"identity":"97d8c7fb-ff5c-436b-9fc2-29460f60ffca","added_by":"auto","created_at":"2025-09-01 07:00:26","extension":"jpg","order_by":15,"title":"Figure 15","display":"","copyAsset":false,"role":"figure","size":456880,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the TC and the moisture flux anomalies during direct TC HPEs and non-HPEs (a and b), indirect TC HPEs and non-HPEs (c and d), and SWM HPEs and non-HPEs (e and f), respectively. The blue and red shades depict enhanced or suppressed moisture flux, respectively. The black arrows indicate the directional anomalies of the moisture flux affecting the Philippines. The TC symbol indicates the location of the TC during the HPE or the non-HPE. The red arrows indicate the direction of the TC.\u003c/p\u003e","description":"","filename":"fig15.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/84a3a8ececb999f042da0d71.jpg"},{"id":94062791,"identity":"ae2f62d4-ec67-4e13-9ce5-4da7964903a5","added_by":"auto","created_at":"2025-10-22 07:16:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":12484204,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/6c1f2cfd-8281-485d-a262-9d0ae042d24e.pdf"},{"id":90288674,"identity":"2d710ea9-216e-4206-8203-3bb7089c6b24","added_by":"auto","created_at":"2025-09-01 07:00:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":8499683,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigures.docx","url":"https://assets-eu.researchsquare.com/files/rs-7273115/v1/0b41c70d0f24b711a43d32fb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Role of the Moisture Conveyor Belt in High Precipitation Events Along the Western Coast of the Philippines During the Southwest Monsoon Season","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe western coast of the Philippines receives around 40\u0026ndash;80% of its total annual rainfall during May to September (Olaguera et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). During these months, the western North Pacific summer monsoon (WNPSM), which is a component of the Asian summer monsoon (Matsumoto, 1992; Murakami and Matsumoto, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), affects the tropical western North Pacific, especially the Philippines. The WNPSM, or the southwest monsoon (SWM) in the Philippine context, is characterized by a series of warm, moist, southwesterly winds, which originate from the Indian Ocean. Due to the warm and moist nature of the winds brought by the SWM, it is usually accompanied by strong convective activity and heavy rainfall, especially when these winds are forced upwards by the mountain ranges near the western coast of Luzon Island (Cayanan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Cruz et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The exact duration of the SWM varies from year-to-year, but studies have estimated the SWM to start as early as mid-May and last as late as mid-November (Kubota et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Matsumoto et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Olaguera and Manalo, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe peak months of the SWM coincide with the peak months of the typhoon season in the western North Pacific basin. Almost 50% of all the TCs that enter the Philippine Area of Responsibility (PAR) occur during July, August, and September (JAS) (Corporal-Lodangco and Leslie, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This results in TCs and the SWM interacting with each other, producing heavy rainfall, especially over the western coast of Luzon Island. Cayanan et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) concluded that TCs northeast of Luzon generate stronger southwesterlies over the island of Luzon. These southwesterlies are then orographically lifted as they move over the mountain ranges near the western coast of Luzon, such as the Cordillera Central and Zambales mountain ranges (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This results in intense rainfall over the western coast of Luzon, leading to floods, even when the TC itself did not make landfall in Luzon.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe amount of rainfall produced by the interaction of TCs and the SWM has been observed to vary from year-to-year. Bathan et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) separated the total rainfall measured over the Philippines into three categories: (1) direct TC rainfall, which is rainfall that directly comes from the TC\u0026rsquo;s rainbands; (2) indirect TC rainfall, which is rainfall produced by the TC\u0026rsquo;s enhancement of the SWM; and (3) SWM rainfall, which is rainfall that comes from the SWM alone, without any TCs in the region. Their study discovered that anomalous circulations, which affect the formation and tracks of TCs, are responsible for the interannual variations observed in direct and indirect TC rainfall. Similar variations are also observed in SWM rainfall, which is due to the yearly change in the strength and moisture transport of the SWM. Years with an inherently strong monsoon and anomalous circulations capable of steering TCs towards the northeast of the Philippines may increase the likelihood of extreme rainfall events produced by the TC-enhanced SWM.\u003c/p\u003e\u003cp\u003eThe occurrence of these extreme rainfall events due to a TC\u0026rsquo;s enhancement of the SWM has often been studied. Bagtasa (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) identified the occurrence of high precipitation events (HPE) over the western coast of the Philippines from 1958 to 2017. The study confirmed the existence of the moisture conveyor belt (MCB) during HPEs, which caused an almost 500% and 700% increase in zonal wind speed and moisture flux over the Philippines, respectively. A subsequent study by Bagtasa (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) characterized the 2012 and 2013 enhanced SWM events over Metro Manila. Both events had a TC to the northeast of the Philippines, which enhanced the southwesterly monsoon flow, and a remnant low over the Vietnam-China border, which created low-level westerly jets over the Indochina Peninsula. These conditions led to the formation of the MCB, which transported large amounts of moisture from the Indian Ocean to the WNP and produced intense rainfall over the Philippines.\u003c/p\u003e\u003cp\u003eThe MCB, also known as an atmospheric river (Guo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), is defined as a continuous band of high moisture fluxes stretching from the tropical north Indian Ocean to the East Asia region (Kudo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Due to the high moisture transport within this band, heavy rainfall can occur where it passes, including the Philippines. Kudo et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) stated that the MCB forms due to the lower tropospheric circulations created by a TC. Additionally, their study stated that the presence of monsoon westerlies from the Indian Ocean to the South China Sea is a prerequisite for the formation of the MCB. This implies that not all TCs can produce an MCB, especially if the monsoon westerlies over the Indian Ocean and the South China Sea are anomalously weak. The presence of anomalously strong westerlies over the Indian Ocean and the South China Sea alone also does not equate to an MCB if a TC is not present. Fujiwara et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) confirmed this by conducting an experiment that artificially removed a TC from a model run. Their results revealed that the TC-less case was unable to produce an MCB, despite the control case (Typhoon Man-yi, 2007) producing one.\u003c/p\u003e\u003cp\u003eDespite the numerous studies regarding HPEs and TC-enhanced SWM cases, the relationship between TC location and intensity with rainfall over the Philippines is still not clear. For instance, Bagtasa (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) stated that not all TCs passing through the northeast of the Philippines lead to enhanced SWM rainfall and HPEs. The atmospheric conditions during cases in which TCs did or did not lead to HPEs, even if the locations of these TCs are similar, should be investigated. This study, therefore, aims to (1) examine the relationship between the identified HPEs and TC location/intensity and (2) examine HPEs associated with direct TCs, indirect TCs, and the SWM only. Determining whether TC location and intensity affect the rainfall amount during HPEs may lead to improved forecasting of HPEs during TC-enhanced SWM cases. Furthermore, finding the difference in synoptic conditions between HPEs and TC-related HPEs through composite analysis may provide insights about the driving factors behind the occurrence of HPEs. The rest of the paper is organized as follows: Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e enumerates the datasets used in the analysis and presents the methodology for this study. Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e examines the relationship between TC location and intensity with rainfall amount of identified HPEs, and the differences in atmospheric conditions between HPEs and non-HPEs. Finally, Section \u003cspan refid=\"Sec16\" class=\"InternalRef\"\u003e4\u003c/span\u003e provides the summary and conclusions of the study.\u003c/p\u003e"},{"header":"2. Data and Methodology","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Data\u003c/h2\u003e\u003cp\u003eThis study uses rainfall data from 1961 to 2022 for the JAS season, which are the peak months of the SWM season in the Philippines (Lyon and Camargo, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The following three datasets are used:\u003c/p\u003e\u003cp\u003e\u003col style=\"list-style-type:lower-alpha;\"\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eDaily rainfall measurements from 11 stations (Ambulong, Baguio City, Coron Island, Cuyo Island, Dagupan City, Iba, Iloilo City, Laoag City, Port Area, Sangley Point, and Science Garden; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) along the western coast of the Philippines, provided by the Department of Science and Technology-Philippine Atmospheric, Geophysical, and Astronomical Services Administration (DOST-PAGASA). These stations are classified as Type I in the Modified Coronas Climate Classification (Kintanar, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1984\u003c/span\u003e), where the influence of the southwest monsoon is more pronounced. The 11 stations were specifically chosen since they have no more than 20% missing data during the analysis period;\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eReanalysis data from the fifth-generation reanalysis dataset of the European Centre for Medium-Range Weather Forecasts (ERA5). The dataset has a spatial resolution of 0.25\u0026deg; \u0026times; 0.25\u0026deg; from 1940 to the present (Hersbach et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e);\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eTC best track data at six-hour intervals from the Joint Typhoon Warning Center (JTWC), which can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metoc.navy.mil/jtwc/jtwc.html?best-tracks\u003c/span\u003e\u003cspan address=\"https://www.metoc.navy.mil/jtwc/jtwc.html?best-tracks\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003eThe Boreal Summer Intraseasonal Oscillation (BSISO) Index (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), which can be accessed at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cliks.apcc21.org/dataset/bsiso\u003c/span\u003e\u003cspan address=\"https://cliks.apcc21.org/dataset/bsiso\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. This data set is available from January 1981 to the present. Only the first mode of the BSISO, with a periodicity of 30\u0026ndash;60 days, was used in this study, since this has been found to modulate the occurrence of extreme rainfall events in the Philippines (Olaguera et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e).\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Methodology\u003c/h2\u003e\u003cp\u003eIn this study, high precipitation days (HPD) are defined as days wherein the observed rainfall amount for at least one station exceeds its 95th percentile rainfall amount during the JAS season. The identified HPDs are then classified into three categories: direct TC HPDs, indirect TC HPDs, and SWM HPDs. Direct TC HPDs are defined as HPDs with a TC that is within 500 kilometers of any of the stations. Indirect TC HPDs are defined as HPDs with a TC inside the study domain (6.34\u0026deg;N\u0026ndash;30\u0026deg;N; 110\u0026deg;E\u0026ndash;135\u0026deg;E; see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), but not within 500 kilometers of any of the stations. SWM HPDs are defined as HPDs without any TCs inside the study domain. The specific distance of 500 kilometers was chosen since several studies have used this to estimate the range of a TC\u0026rsquo;s direct rainbands (Yokoyama and Takayabu, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Lau et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Nogueira and Keim, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Prat and Nelson, 2012; Dare et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; and Khouakhi et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While some studies, such as Kubota and Wang (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), have shown that TC rainfall can reach up to ~\u0026thinsp;1000 kilometers away from the TC\u0026rsquo;s center, this study used a 500-kilometer radius to be more certain that the recorded rainfall is directly from the TC itself. Using a\u0026thinsp;~\u0026thinsp;1000-kilometer radius may inadvertently identify TC-enhanced SWM rain as direct TC rain, especially if the TC is not very large. Additionally, JTWC defines a medium-sized TC as a TC with a radius of 3\u0026deg; to 6\u0026deg;. Assuming a radius of 500 kilometers, which is around 4.5\u0026deg;, puts it very close to the middle of JTWC\u0026rsquo;s TC size chart (JTWC, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo visualize the spatial distribution of the TCs during each HPD, the TC position of each direct TC and indirect TC HPD was plotted. The distance between the center of the TCs and the PAGASA stations was calculated to determine if there is a relationship between the TC distance and the rainfall amount during the HPD. The intensity of the TC during the HPD was used to determine the relationship between TC intensity and the rainfall amount. Sea surface temperature (SST) and specific humidity composites were also created to reveal any correlation between these variables and HPDs.\u003c/p\u003e\u003cp\u003eThe identified HPDs during the entire analysis period were then grouped into individual high precipitation events (HPEs). An HPE is defined here as a group of HPDs that are not more than five days apart. This ensures that cases with a TC producing consecutive HPDs will only be counted as one HPE. Lag composites of the HPEs were then created to visualize the temporal evolution of the HPE and possibly reveal any precursors to an HPE. The longitudinal range of the composite analysis is from 45\u0026deg;E to 180\u0026deg;E. This allows for the Arabian Sea to be included in the analysis in addition to the Bay of Bengal, South China Sea, and the WNP, which are the regions usually analyzed in studies regarding the MCB (Kudo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fujiwara et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The inclusion of the Arabian Sea is due to the study by P\u0026eacute;rez-Alarc\u0026oacute;n et al. (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who discovered that the Arabian Sea is also a moisture source for TCs around the vicinity of Taiwan and the northern Philippines. Additionally, only one HPE was chosen per year for the lag composites. Since August usually has the most HPDs (followed by July, then September), the chosen HPE is the first HPE during August. If no HPEs occurred in August of that year, the last HPE during July will be chosen. If there were no HPEs in both July and August, the first HPE during September will be chosen.\u003c/p\u003e\u003cp\u003eTo confirm whether the presence of the MCB is the main cause of HPEs, composites of both HPEs and non-HPEs were created. Non-HPEs are defined as a group of days wherein none of the stations recorded HPD-level rainfall. Three non-HPEs were chosen for each year of the analysis period. The first non-HPE that was chosen had a TC in roughly the same location as the direct TC HPE. This served as the direct TC non-HPE dataset. The second non-HPE chosen had a TC in roughly the same location as the indirect TC HPE. This served as the indirect TC non-HPE dataset. Finally, the third non-HPE chosen had no TCs in the domain. This served as the SWM non-HPE dataset.\u003c/p\u003e\u003cp\u003eTo identify the existence and structure of the MCB, lag composite anomalies of water vapor flux and surface latent heat flux were created, following Kudo et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Fujiwara et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) to visualize the extent of the MCB.\u003c/p\u003e\u003cp\u003eTo test for statistical significance of the composites, a bootstrap \u003cem\u003et\u003c/em\u003e-test (Efron and Tibshirani, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) was conducted relative to all JAS days during the analysis period. The regular Student\u0026rsquo;s \u003cem\u003et\u003c/em\u003e-test was repeated 1000 times; each iteration with a different, random sample to obtain the bootstrapped \u003cem\u003ep\u003c/em\u003e-value. A bootstrapped \u003cem\u003ep\u003c/em\u003e-value of less than 0.05 signifies anomalies that are statistically significant at the 95% confidence level and are, therefore, marked in the composites.\u003c/p\u003e\u003cp\u003eLastly, the moisture flux anomalies over the Arabian Sea, the Bay of Bengal, and the southern half of the South China Sea were analyzed to determine any daily variations, which may increase or decrease the likelihood of an HPE occurring over the Philippines. The extent of these analysis regions are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The region R1 (10\u0026deg;N\u0026ndash;20\u0026deg;N; 55\u0026deg;E\u0026ndash;75\u0026deg;E) encompasses the Somali Jet, which is a strong southwesterly wind over the Arabian Sea during the Asian summer monsoon season. The Somali Jet is also believed to precede the onset of the monsoon rainfall over the western coast of India (Halpern and Woiceshyn, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). R2 (10\u0026deg;N\u0026ndash;20\u0026deg;N; 80\u0026deg;E\u0026ndash;95\u0026deg;E) encompasses the Bay of Bengal and is dominated by strong westerlies during the Asian summer monsoon season. R3 (5\u0026deg;N\u0026ndash;15\u0026deg;N; 105\u0026deg;E\u0026ndash;120\u0026deg;E) encompasses the southern half of the South China Sea and features strong winds and high moisture flux values during TC-enhanced SWM cases in the WNP. A time series of the moisture flux anomalies over these three regions for three specific years was created, and the days wherein an HPE occurs over the western coast of the Philippines were marked to see the relationship between these anomalies and HPE occurrence. These regions were chosen as they showed significant anomalies in the lag composite analysis that will be discussed in Section \u003cspan refid=\"Sec5\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003e3.1 High precipitation days over the Philippines\u003c/h2\u003e\u003cp\u003eOut of the 5,642 days in the analysis period, 1,554 days were classified as HPD. Most HPDs occurred in the month of August (605 HPDs), followed by July (522 HPDs), then September (427 HPDs). Direct TC, indirect TC, and SWM HPDs accounted for 15.0%, 32.6%, and 52.4% of all the HPDs, respectively. The breakdown for each type of HPD for every station is shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of direct TC, indirect TC, and SWM HPDs for each station during the months of July, August, and September from 1961 to 2022. Values inside parentheses indicate the percentage breakdown of each HPD type for that particular station. The 95th percentile rainfall value for each station is included to show each station\u0026rsquo;s HPD rainfall threshold. The stations are arranged by latitude with Laoag as the northmost and Iloilo as the southmost in order to easily see the spatial trend.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDirect TC HPDs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndirect TC HPDs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSWM HPDs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHPD rainfall threshold (mm)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaoag\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e82 (28.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e116 (40.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e88 (30.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e80.96\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaguio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72 (25.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e159 (55.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e55 (19.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e110.54\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDagupan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e52 (18.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e138 (48.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e96 (33.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e72.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e141 (49.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e111 (48.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e117.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScience Garden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e24 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e127 (45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e131 (46.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e68.30\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePort Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e121 (45.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e129 (48.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSangley Point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19 (8.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e115 (51.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e90 (40.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e63.11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmbulong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e34 (11.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e130 (45.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e122 (42.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e46.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoron\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11 (4.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e109 (39.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e158 (56.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e67.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCuyo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e8 (2.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e77 (28.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e187 (68.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e52.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIloilo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e10 (4.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e97 (39.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e136 (56.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e51.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThere appears to be a clear correlation between the number of direct TC HPDs and the latitude of the station. Stations with a higher latitude have a higher direct TC HPD percentage, while stations with a lower latitude have a lower direct TC HPD percentage. This can be attributed to the nature of TCs during the JAS season when there are fewer TCs landfalling in the Philippines as they move north or northeast of the Philippines (Corporal-Lodangco and Leslie, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Meanwhile, the correlation with latitude seems to be a lot less prominent with the indirect TC HPD percentage. Similar to the direct TC HPDs, the three southernmost stations have the lowest indirect TC HPD percentage; however, the station with the fourth lowest indirect TC HPD percentage is the Laoag station, which is the northernmost station. Additionally, unlike the direct TC HPDs, where there was a steep drop in percentage past the Dagupan station, the indirect TC HPD percentages hover around 45% until the Ambulong station, except for the previously mentioned Laoag station. It may be possible that indirect TC HPD percentages depend less on station latitude and more on local topography. SWM rainfall over the Philippines is often produced due to the orographic lifting of the moist monsoon winds by the mountain ranges on the western coast of the country (Cayanan, 2011; Lagmay et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Cuyo Island is a fairly flat island, with the highest point only having an elevation of 249 meters above sea level. This may explain why the Cuyo station has the lowest indirect TC HPD percentage. Baguio, on the other hand, has an elevation of 1,510 meters. It is also located on the windward side of the Cordillera Central mountain range (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, CC), which may explain why Baguio station has the highest indirect TC HPD percentage. Sangley Point, the station with the second highest indirect TC HPD percentage, is not directly on the windward side of any mountain range. However, Lagmay et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) concluded that the volcanoes in the southern region of the Zambales mountain range (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, ZM) can induce heavy rainfall over Greater Metro Manila\u0026mdash;which includes the Sangley Point station\u0026mdash;despite it not being on the windward side of the volcanoes. Iba, the station with the third highest indirect TC HPD percentage, is on the windward side of the Zambales mountain range (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, ZM). Meanwhile, a possible cause for the fairly low indirect TC HPD percentage for the Laoag station is the position of the mountain range. For the previously mentioned stations with high indirect TC HPD percentages, the high mountains are located directly to the northeast of the station. Since monsoon winds flow from the southwest, this means that these stations are directly on the windward side. For the case of Laoag, the Cordillera Central mountain range (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, CC) does not fully cover the northeastern portion of the station. A small gap is present between the northern coast of Luzon and the northern point of the Cordillera Central mountain range (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, CC). This gap does not have a very high elevation, which may lead to a weaker orographic lifting effect. Finally, the number of SWM HPDs is dependent on the number of TC-related HPDs simply because these are the HPDs that were not classified as direct or indirect TC HPDs. More TC-related HPDs mean fewer SWM HPDs for that specific station and vice versa.\u003c/p\u003e\u003cp\u003eDue to the nature of how HPDs are identified, some HPDs featured multiple stations recording HPD-level rainfall. The breakdown of how many individual stations simultaneously recorded HPD-level rainfall for each kind of HPD is shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. HPDs that affect three or more stations at a time can be considered as widespread HPDs. The percentage of widespread HPDs is calculated by summing the percentages in rows 3 through 11 of Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It is worth noting that the percentages of widespread HPDs for the direct TC, indirect TC, and SWM categories are 42.1%, 28.5%, and 14.8%, respectively. This implies that direct TC cases bring HPD-level rainfall over a larger area compared to indirect TC and SWM cases. This makes sense as TCs have a large rainfall radius, spanning around 500 kilometers from the center of the TC (Yokoyama and Takayabu \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2008\u003c/span\u003e), which leads to heavy TC rainfall over large regions at a time. Meanwhile, SWM HPDs are most likely cases of localized thunderstorms, which only affect one station at a time. This is reflected in the percentage of HPDs where only one station recorded HPD-level rainfall for the SWM category, which is 68.7%. This percentage is significantly higher than the direct TC and indirect TC percentages, which are 36.1% and 50.1%, respectively. The percentages of the indirect TC HPDs in both single-station HPDs and widespread HPDs sit between the direct TC and SWM percentages. TCs in the indirect TC HPDs are usually not close enough to land; therefore, the TCs rainbands do not encompass large areas of the country. Instead, rainfall during indirect TC cases is from the enhancement of the SWM. The enhancement of the SWM forms an MCB, which is a band of high moisture flux that traverses Luzon. This band encompasses a fairly large area of land, which may be the reason why there are more widespread HPDs in the indirect TC category than the SWM category. However, this band usually traverses Luzon at W-E or a SW-NE orientation, which means the length of the band does not cover all the stations at once, since the stations roughly form a N-S oriented line. This may be the reason why there are still more widespread HPDs in the direct TC category than in the indirect TC category.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBreakdown of how many stations simultaneously experienced HPD-level rainfall during each of the Direct TC, indirect TC, and SWM HPDs during the months of July, August, and September from 1961 to 2022. Values inside parentheses are the percentages of that specific HPD type with anomalies over each region.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of stations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTotal HPD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDirect TC HPD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIndirect TC HPD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSWM HPD\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e900 (57.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e84 (36.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e256 (50.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e560 (68.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e291 (18.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e51 (21.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e106 (20.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e134 (16.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e153 (9.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e41 (17.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e55 (10.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e57 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e93 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e23 (9.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36 (7.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e34 (4.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e55 (3.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 (5.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e26 (5.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e16 (2.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e14 (6.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e12 (2.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11 (1.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16 (1.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (1.7)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9 (1.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3 (0.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8 (0.5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (0.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e6 (1.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e3.1.1 TC characteristics during HPDs\u003c/h2\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the TC positions during all TC-related HPDs in the analysis period. The average coordinates of all direct TCs and indirect TCs were calculated to determine the mean TC positions during direct TC HPDs (yellow \u0026ldquo;+\u0026rdquo;) and indirect TC HPDs (yellow \u0026ldquo;\u0026times;\u0026rdquo;). The direct TC mean position is located near the northeastern tip of Luzon Island. This supports Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, wherein the direct TC HPD percentage is higher in the northern stations. The indirect TC mean position is located to the northeast of the Philippines, which is consistent with previous studies that concluded that TCs to the northeast of the Philippines can bring intense rainfall to the western coast of Luzon by enhancing the SWM (Cayanan et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Bagtasa, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Bathan et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIt can be observed that the majority of the TC points are located north of the 15\u0026deg;N latitude line. This, again, is due to the nature of the TC tracks during these months. Furthermore, it can also be observed that the majority of the TC points are located to the east of 120\u0026deg;E. This may simply be because there are more TCs that form over the Pacific Ocean compared to the South China Sea, but it may also be due to how the rainbands of the TC and the enhanced SWM interact with each other. A TC east of 120\u0026deg;E causes the enhanced SWM to traverse over Luzon Island. On the other hand, a TC west of 120\u0026deg;E only enhances the SWM over the South China Sea, especially if the TC is much closer to mainland Asia than to the Philippines.\u003c/p\u003e\u003cp\u003eThe position of each TC during all TC-related HPDs relative to each station is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. This was done to see the differences in TC positions during HPDs for different stations. It can be observed that both the direct and indirect TC mean positions seem to be related to the latitude of the stations. The mean direct and indirect TC positions for Laoag station, the northernmost station, are both between NE and ENE, while the mean direct and indirect TC positions for Iloilo station, the southernmost station, are both between N and NNE. Additionally, the distance between the indirect TC mean position and the station is the greatest for the Iloilo station and the least for the Laoag station. These two observations can simply be because of the TC tracks during this season; therefore, they are much closer and more to the east in stations such as Laoag. The percentage of TCs inside each quadrant (Q1 to Q4, counterclockwise from the northeast quadrant) was calculated and tabulated in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e to determine the general direction of the TC during TC-related HPDs for each station. The table reveals that the majority of the TC-related HPDs occur when a TC is in Q1. The only exception is the direct TC HPDs for Cuyo, in which Q2 is the quadrant with the highest TC percentage. It is worth noting that for the direct TC HPDs, the quadrant with the second highest percentages is generally Q4. Meanwhile, for the indirect TC HPDs, the quadrant with the second highest percentages is generally Q2. This difference in quadrants is due to how direct TCs affect the stations compared to indirect TCs. TCs that directly affect the country usually travel northwestward; therefore, a TC coming directly towards a station, which produces HPD-level rainfall, will appear to the southeast of the station, which is Q4. On the other hand, indirect TCs usually only produce rainfall if the TC is to the north of the station. Cayanan et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) discussed a case wherein a TC was still able to produce extreme rainfall despite being to the northwest of Luzon Island, rather than the usual northeast position like with most of the TC-enhanced SWM cases. Additionally, for the direct TC HPDs, the Q2 percentages are lower than the Q4 percentages since there are significantly fewer TCs that form in the South China Sea region and affect the stations from the southwest compared to TCs that form in the Pacific Ocean or the Philippine Sea. For the indirect TC HPDs, the Q4 percentages are lower than the Q2 percentages since indirect TC HPDs are usually TC-enhanced SWM cases. The position of the TC must be to the north of the station for the rain bands of the TC-enhanced SWM to produce rainfall over the stations.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePercentage of TC points on each quadrant for every TC-related HPD in every station from 1961 to 2022. Q1, Q2, Q3, and Q4, are the NE, NW, SW, and SE quadrants, respectively.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eStation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e\u003cp\u003eDirect TC HPDs\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"4\" nameend=\"c9\" namest=\"c6\"\u003e\u003cp\u003eIndirect TC HPDs\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaoag\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e62.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e13.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e24.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e76.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e6.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e13.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaguio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e72.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e19.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e9.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e8.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e3.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDagupan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e71.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e17.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e82.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e11.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e5.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e11.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e83.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e14.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScience Garden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e80.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePort Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e68.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e31.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e81.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e17.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSangley Point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e63.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e10.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e80.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmbulong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e73.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e17.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e75.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e16.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e7.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoron\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e9.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e18.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e88.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e10.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCuyo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e75.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e71.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e26.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIloilo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e50.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e40.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e10.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e80.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e19.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePerhaps a more interesting analysis is the relationship between TC distance and intensity with HPD rainfall amount. Figure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e shows the number of HPDs depending on the distance of the TC from the station, in 100-kilometer increments. The mean rainfall amount for all the HPDs inside each 100-kilometer increment is also shown. The TC distance that produces the most HPDs per station seems to increase in the stations closer to the equator. This may simply be because TCs that produce HPDs are usually located further north. Interestingly, only the first two stations from the north (Laoag and Baguio stations) follow the expected downward trend of mean rainfall amount as the distance between the TC and the station increases, as seen by the negative Pearson correlation coefficients for these stations in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. For the rest of the stations, it seems that rainfall is not dependent on the TC distance since the correlation coefficients for these stations do not exceed\u0026thinsp;\u0026plusmn;\u0026thinsp;0.5, which is the threshold to be considered as a strong correlation (Turney, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, only the \u003cem\u003ep\u003c/em\u003e-values of the first two stations show statistical significance at the 95% confidence level.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson correlation coefficients for the mean rainfall amount during HPDs and the TC location and intensity. Bold values denote values that are statistically significant at the 95% confidence level.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHPD mean rainfall and TC distance\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHPD mean rainfall and TC intensity\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLaoag\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.75\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBaguio\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e-0.55\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDagupan\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIba\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.08\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScience Garden\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePort Area\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSangley Point\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAmbulong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoron\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e-0.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCuyo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e-0.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIloilo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the number of HPDs depending on the intensity of the TC, in 5-knot increments. The mean rainfall amount for all the HPDs inside each 5-knot increment is also shown. The mean HPD rainfall amount does not seem to be related to TC intensity. This is supported by Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, which does not indicate a correlation between TC intensity and HPD rainfall amount since none of the stations shows a correlation coefficient that is statistically significant at the 95% confidence level. This is surprising since more intense TCs should produce stronger southwesterlies, which should produce heavier rainfall. This does not seem to be the case, especially since some stations show very subtle decreasing rainfall trends as TC intensity increases (Laoag, Science Garden, Port Area, Sangley Point, and Cuyo Island). This may suggest that there is something else that is modulating enhanced monsoon rainfall over the western coast of Luzon, and not just TC distance and intensity. Additionally, for all the stations, a decreasing trend in the number of HPDs can be observed as TC intensity increases. This is possibly due to the fact that less intense TCs are more common than very intense ones.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe HPD rainfall amount not displaying a correlation with TC distance and intensity implies that there must be different factors that directly affect the rainfall amount observed over the Philippines during TC-enhanced SWM events. This may be where the MCB comes in, andits presence might be a significant factor in the occurrence of HPDs.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e3.1.2 Sea surface temperature and specific humidity\u003c/h2\u003e\u003cp\u003eSST and specific humidity composites were also analyzed to reveal any anomalies that might explain the extreme rainfall during HPDs. Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e shows the SST composite anomalies for direct TC, indirect TC, and SST HPDs. For a better comparison, composite anomalies of their respective non-HPDs were also created, as well as their respective differences (HPD minus non-HPD). All TC-related cases, whether HPD or non-HPD, exhibit SST cooling in the waters surrounding the Philippines. Studies by Wang et al. (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and Kubota et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) stated that summer monsoon rainfall over Luzon has a negative correlation with the surrounding SSTs, which can be attributed to the rainfall produced by the monsoon cooling the affected waters. This cooling effect by rainfall is not just limited to monsoon rainfall, but also occurs with TC rainfall, which may explain the large patches of negative SST anomalies around the Philippines in the direct TC composites (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ea and S1d) and to the northeast of the Philippines in the indirect TC composites (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eb and S1e) since these regions coincide with the usual location of the TC during these cases. SST cooling can also be observed outside the extent of the TC\u0026rsquo;s rainbands. For example, the cooling observed in the South China Sea in all the TC-related composites cannot simply be attributed to TC rainfall, as the TC is not located over this region. The cooling observed in this region, as well as in the tropical northern Indian Ocean for the indirect TC cases, may be due to the increased rainfall, greater cloud cover, and stronger winds of the enhanced SWM. Additionally, the SST cooling observed in these regions is greater in magnitude during HPDs compared to non-HPDs, which can be clearly seen in the difference plots of the TC-related cases (Figures \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ec and S1f). This is expected as there is more rainfall produced by the enhanced SWM during HPDs compared to non-HPDs. This cooling pattern over the tropical Indian Ocean is consistent with the findings of Hegde et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The study stated that higher SSTs in the region may weaken the structure of the MCB, whereas lower SSTs may strengthen it. This SST-MCB relationship was also validated in the SST experiments of Fujiwara et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). A more structured MCB may lead to more rainfall over the Philippines, which explains the lower SSTs in the tropical Indian Ocean during HPDs. For SWM HPDs, despite not having a TC, cases of monsoon surges may produce SWM HPDs and subsequently cool the waters in the South China Sea. The opposite can be seen in the SWM non-HPD composite (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eh), which shows positive SST anomalies in almost the entire tropical region. This heating may be due to the lack of rainfall, cloud cover, and winds due to the suppression of the monsoon over these regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAnother interesting finding is the significant heating of the ocean waters north of 30\u0026deg;N during both indirect TC composites and the SWM HPD composite. Bathan et al. (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) discovered a similar SST pattern over this region during years with high and low SWM rainfall over the Philippines. The SSTs around this region seemed to be higher during the years with high SWM rainfall compared to the years with low SWM rainfall. Additionally, Liu et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) also had similar findings, wherein SSTs around Japan were higher in strong monsoon years compared to weak monsoon years. In the case of HPDs, SWM HPDs may occur more often during strong monsoon years, while SWM non-HPDs occur more often during weak monsoon years. This may explain the similar SST pattern. However, the exact reason for this SST pattern is unknown and may need further investigation.\u003c/p\u003e\u003cp\u003eFigure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e is a similar set of composites to Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, but for vertically integrated specific humidity. The main takeaway in these composites is the significantly higher moisture content around the TC, over the Philippines, the South China Sea, and the Indochina Peninsula during the HPD composites. The higher moisture content signified by the high specific humidity values may be directly related to the higher rainfall amount experienced during HPDs. However, the structure of the MCB is not fully captured by vertically integrated specific humidity alone.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Comparing HPEs and non-HPEs\u003c/h2\u003e\u003cp\u003eBagtasa (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) emphasized that the establishment of the MCB is necessary for the occurrence of extreme rainfall during TC-enhanced SWM events. Therefore, with the use of lag composites, this section will attempt to determine if the presence of the MCB is related to the occurrence of HPDs. First, the identified HPDs were grouped into separate HPEs. HPDs that are within 5 days of each other were counted as one HPE. This is to ensure that weather systems that last for multiple days and produce consecutive HPDs, such as TCs, are only counted as one event. This grouping resulted in 139 direct TC HPEs, 230 indirect TC HPEs, and 375 SWM HPEs. On average, a direct TC HPE lasted 1.88 days, an indirect TC HPE lasted 2.79 days, and an SWM HPE lasted 2.94 days. The short duration of direct TC HPEs is due to the average translational speed of a TC, which is 19 kilometers per hour (PAGASA, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). With this translational speed, it would take a TC 2.19 days to cross the 1000-kilometer diameter circle around a station. Since most TCs do not cross the circle at its widest point, the actual duration is shorter than 2.19 days. Indirect TC HPEs have a longer duration than direct TC HPEs since the region where a TC can enhance the SWM is significantly larger than the areas of the circles around a station. For example, a TC can track northeast of the Philippines for several days before making landfall in Taiwan or eastern China. During these days, the TC can produce heavy rainfall over Luzon by enhancing the SWM. This is exactly what happened with Typhoon Haikui in 2012, which brought heavy rainfall over the Philippines from August 6\u0026ndash;10 (Bagtasa, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Lastly, SWM HPEs lasting the longest may be the result of monsoon surges, which bring strong rainfall over one station at a time. As the monsoon surge evolves, a different station may experience strong rainfall, but since these HPDs may be less than five days apart, they are counted as one HPE. Cayanan et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) discussed a heavy rainfall event without a TC that lasted for several days, from August 17\u0026ndash;21, 2006.\u003c/p\u003e\u003cp\u003eFor the HPE lag composites, the first day of the HPE is assigned as lag 0. The preceding days are lag \u0026minus;\u0026thinsp;5 to -1, while the succeeding days are lag\u0026thinsp;+\u0026thinsp;1 to +\u0026thinsp;5. For the non-HPE lag composites, lag 0 in TC-related non-HPEs is the day when the TC position is similar to that of the TC position during the lag 0 composite of the direct or indirect TC HPEs. Lag 0 in SWM non-HPEs is the first day of a group of days that did not record HPD-level rainfall. This group of days is composed of at least five consecutive non-HPDs. Composites of vertically integrated water vapor flux and surface latent heat flux were used in this section to possibly identify the presence of the MCB, since these are the variables that can depict the structure of the MCB (Kudo et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Fujiwara et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e\u003cdiv id=\"Sec10\" class=\"Section3\"\u003e\u003ch2\u003e3.2.1 Direct TC cases\u003c/h2\u003e\u003cp\u003eThe composite anomalies from lag \u0026minus;\u0026thinsp;5 to lag 0 for the direct TC cases are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The direct TC HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ea to \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003ef) and the direct TC non-HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eg to \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003el) both show the evolution of a TC, depicted as the large circular region of very high positive moisture flux anomalies, which originates in the Philippine Sea, travels west-northwestwards, and crosses the northern tip of Luzon. The location of the TC for each time step is similar for both the HPE and non-HPE composites, which means that the selection of the direct TC non-HPEs was effective. A noticeable difference in the TC structure between the HPE and non-HPE composites is the size of the region of high positive moisture flux anomalies. The region of the moisture flux anomalies within the TC is noticeably larger for the HPE composites compared to the non-HPE composites. This makes sense as TCs with more moisture may bring more rainfall, which then causes the HPEs. The most striking difference between the two kinds of composites is the presence of positive anomalies over some regions of the northern Indian Ocean. For each time step in the HPE composites, a long band of positive moisture flux anomalies can be seen stretching from the Arabian Sea to the TC. These anomalies are present well before the TC appears in the lag composites, as seen in the lag \u0026minus;\u0026thinsp;5 and \u0026minus;\u0026thinsp;4 in the HPE composites (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ea and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003eb). This implies that the presence of TC is not necessarily the cause of these anomalies. During this time, the positive moisture flux anomalies are mostly over the Arabian Sea and the Bay of Bengal, as well as over the Indian subcontinent. As the TC\u0026rsquo;s structure develops (Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ec, \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ed, and \u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ee), the positive anomalies between 100\u0026deg;E and 120\u0026deg;E start to intensify and connect the moisture flux anomalies over the northern Indian Ocean to the anomalies of the TC. Finally, at lag 0 (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003ef), nearly the entire Philippines is covered by the high positive moisture flux anomalies from the MCB and the TC. On the other hand, in the non-HPE composites, the Arabian Sea, Indian subcontinent, Bay of Bengal, and Indochina Peninsula barely show positive moisture flux anomalies in any of the lag composites. Only during the lag \u0026minus;\u0026thinsp;1 and lag 0 composites can a very small region of positive moisture flux anomalies be observed over the southern half of the South China Sea. This may imply a local enhancement of the SWM due to the TC, but not nearly as intense and as widespread as the ones seen in the HPE composites.\u003c/p\u003e\u003cp\u003eNext, the composite anomalies from lag 0 to lag\u0026thinsp;+\u0026thinsp;5 for the direct TC cases are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Much like Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e7\u003c/span\u003e, the TC locations in the direct TC HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003ea to \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003ef) and the direct TC non-HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003eg to \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003el) are very similar. After affecting northern Luzon, the TC continues to travel west-northwestward until it makes landfall over southern China. The HPE composites show positive moisture flux anomalies over the Arabian Sea and the Bay of Bengal even after the TC and the MCB dissipate in the lag\u0026thinsp;+\u0026thinsp;5 composite. On the other hand, the non-HPE composites do not show a single trace of the MCB aside from very small and localized anomalies over some regions in the northern Indian Ocean. For the HPE composites, it is worth noting that the positive moisture flux anomalies move away from the Philippines starting lag\u0026thinsp;+\u0026thinsp;3. This is due to the position of the TC at this point in time. The TC is far enough west of the Philippines that the moisture from the enhanced SWM does not reach the Philippines anymore, as it wraps around the rear flank of the TC instead of continuing northwestwards. These composites also show an enhancement in the East Asian monsoon (EAM) from lag 0 to lag\u0026thinsp;+\u0026thinsp;4 of the HPE case (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003ea to \u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e8\u003c/span\u003ee). Positive moisture flux anomalies are present to the northeast of the TC, affecting the Korean Peninsula and some parts of Japan. These anomalies complete the entire structure of the MCB. This shows the large-scale moisture transport from the Indian Ocean to Japan and the Korean Peninsula via the MCB, as stated by Kudo et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The presence of the positive anomalies throughout the entire region, even after the TC dissipates, may imply that these positive moisture flux anomalies over the northern Indian Ocean last at least 10 days. However, it is still unclear whether or not the passing of the TC lengthened the duration of these anomalies. For the non-HPE composites, the dissipation of the TC seems to be quicker than that of the HPE TC. This may be due to non-HPE direct TCs being weaker in general.\u003c/p\u003e\u003cp\u003eFor the surface turbulent latent heat flux, composite anomalies from lag \u0026minus;\u0026thinsp;5 to lag 0 for the direct TC cases are shown in Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e. Similar to the direct TC moisture flux anomalies, the direct TC HPE composites (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ea to S3f) and the direct TC non-HPE composites (Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003eg to S3l) of the surface latent heat flux both show the evolution of a TC, depicted as the large circular region of positive surface latent heat flux anomalies. Similar to the direct TC moisture flux composites, the size of the anomalies representing the TC seems to be smaller in the non-HPE composites compared to the HPE composites, which further suggests that direct TCs during non-HPEs are less intense or hold less moisture compared to direct TCs during HPEs. In the HPE composites, positive surface latent heat flux anomalies can be observed over the Arabian Sea and the Bay of Bengal for all time steps. There also seems to be small positive surface latent heat flux anomalies over the southern half of the South China Sea, which increase in magnitude once the TC starts to become more organized (Figures \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ed, S3e, and S3f). In the non-HPE composites, only minimal patches of positive anomalies can be observed over the tropical northern Indian Ocean. The development of the TC (Figures \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003ej, S3k, and S3l) still does not produce any significant positive anomalies outside the WNP, implying that the MCB during this case simply does not exist.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe composite anomalies from lag 0 to lag\u0026thinsp;+\u0026thinsp;5 for the direct TC cases are shown in Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e. The TC locations in the direct TC HPE composites (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003ea to S4f) and the direct TC non-HPE composites (Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eg to S4l) are, once again, very similar. The HPE composites continue to show the entire structure of the MCB as a long band of positive surface latent heat flux anomalies even after the TC dissipates in the lag\u0026thinsp;+\u0026thinsp;5 composite, which, again, suggests that these anomalies are independent of the TC. On the other hand, the non-HPE composites do not feature any surface latent heat flux anomalies aside from the positive anomalies present over the South China Sea as the TC tracks to the northwest of the Philippines. This implies that, while there is some kind of enhancement of the SWM due to the TC, the enhancement is confined within the WNP and does not have the same spatial extent as a fully developed MCB.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section3\"\u003e\u003ch2\u003e3.2.2 Indirect TC cases\u003c/h2\u003e\u003cp\u003eNext, the composite anomalies from lag \u0026minus;\u0026thinsp;5 to lag 0 for the indirect TC cases are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The indirect TC HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003ea to \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003ef) and the indirect TC non-HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003eg to \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003el) show very similar TC locations throughout all the time steps, which means that the selection of the indirect TC non-HPEs was effective. The TC during the indirect TC case forms slightly more to the east and deeper into the Pacific Ocean compared to the direct TC case. The TC then takes on a northwesterly track and moves towards the east of Taiwan, unlike in a direct TC case, wherein the TC passes over the northern tip of Luzon. Similar to the direct TC composites, the structure of the MCB in the indirect TC HPE composites is significantly more defined compared to the non-HPE composites, although the non-HPE composites do show a band resembling the MCB, albeit weaker. Additionally, the TC itself seems to have higher anomalies and encompasses a larger region in the non-HPE composites compared to the HPE composites, which may either suggest that TCs in the non-HPE composites are less intense than the TCs in the HPE composites or that TCs do not need to be of high intensity to produce HPEs. Just like in the direct TC HPE composites, the MCB in the indirect TC HPE composites appears before the TC forms (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003ea and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003eb). During this time, very strong positive anomalies are present over the Arabian Sea, the Indian subcontinent, and the Bay of Bengal. These anomalies also stretch across the Indochina Peninsula and towards the South China Sea, but are significantly weaker, especially past 100\u0026deg;E. As the TC starts to become organized during lag \u0026minus;\u0026thinsp;3 and \u0026minus;\u0026thinsp;2 (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003ec and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003ed), the moisture flux anomalies extend over the South China Sea due to the TC\u0026rsquo;s enhancement of the SWM. By lag \u0026minus;\u0026thinsp;1 and 0 (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003ee and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003ef), the MCB fully forms as the moisture flux anomalies extend towards the TC. At this point, high positive moisture flux values can be seen over the southern tip of the Indochina Peninsula, the southern half of the South China Sea, and the central portion of the Philippines. In the non-HPE composites, the spatial characteristics of the anomalous band of moisture flux are similar to those of MCB as seen in the HPE composites, but are significantly weaker, especially around the South China Sea region in the lag \u0026minus;\u0026thinsp;1 and 0 composites (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003ek and \u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003el).\u003c/p\u003e\u003cp\u003eThe composite anomalies from lag 0 to lag\u0026thinsp;+\u0026thinsp;5 for the indirect TC cases are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The indirect TC HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e10\u003c/span\u003ea to \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e10\u003c/span\u003ef) and the indirect TC non-HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e10\u003c/span\u003eg to \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e10\u003c/span\u003el) show the same TC track progression. The TC continues its northwestward trajectory and makes landfall over the northern tip of Taiwan and eventually in eastern China. For the HPE composites, the Philippines experiences its highest moisture flux anomalies during lag\u0026thinsp;+\u0026thinsp;1 and lag\u0026thinsp;+\u0026thinsp;2. This may imply that these are the peak days of indirect TC HPEs. Unlike those in the direct TC HPE composites, wherein the enhanced SWM stops affecting the Philippines by lag\u0026thinsp;+\u0026thinsp;3, the enhanced SWM in the indirect TC HPE case continues to affect the Philippines even up until lag\u0026thinsp;+\u0026thinsp;5. In the lag\u0026thinsp;+\u0026thinsp;4 composites for both cases (Figs.\u0026nbsp;\u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e10\u003c/span\u003ee and \u003cspan refid=\"Fig14\" class=\"InternalRef\"\u003e10\u003c/span\u003ek), similar to the direct TC HPE composites, positive moisture flux anomalies appear to the northeast of the dissipating TC. These anomalies imply an enhancement in the EAM; however, the area of enhancement is slightly different between the HPE and non-HPE cases. In the HPE case, the enhanced EAM is over the Korean Peninsula; in the non-HPE case, the enhanced EAM is over Japan. Additionally, the enhanced EAM during the HPE case completes the entire structure of the MCB, once again showing the large-scale moisture transport from the Indian Ocean to the Korean Peninsula via the MCB. From lag\u0026thinsp;+\u0026thinsp;3 to lag\u0026thinsp;+\u0026thinsp;5 of the HPE composites, the MCB continues to linger over the South China Sea and the Philippines even after the TC starts to dissipate, which indicates that these areas may continue to experience rainfall. This may be the reason why indirect TC HPEs last slightly longer than direct TC HPEs. For the non-HPE composites, the moisture flux anomalies over the Philippines in lag\u0026thinsp;+\u0026thinsp;1 and lag\u0026thinsp;+\u0026thinsp;2 are not as high as the ones in the HPE composites. By lag\u0026thinsp;+\u0026thinsp;3, only the northwestern coasts of the Philippines are affected by the enhanced SWM. By lag\u0026thinsp;+\u0026thinsp;4 and lag\u0026thinsp;+\u0026thinsp;5, the Philippines is covered by subtle negative moisture flux anomalies, which are the total opposite of the lag\u0026thinsp;+\u0026thinsp;4 and lag\u0026thinsp;+\u0026thinsp;5 of the HPE composites. This implies that the rainfall in the non-HPE cases lasts shorter than in the HPE cases.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe surface latent heat flux composite anomalies from lag \u0026minus;\u0026thinsp;5 to lag 0 for the indirect TC cases are shown in Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e. The indirect TC HPE composites (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003ea to S5f) and the indirect TC non-HPE composites (Figure \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003eg to S5l) both show the evolution of the TC from lag \u0026minus;\u0026thinsp;2 and onwards. The TC in the non-HPE composites seems to have higher surface latent heat flux anomalies compared to the TC in the HPE composites, similar to the indirect TC moisture flux composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e9\u003c/span\u003e), which further suggests that the TC in the non-HPE composite is more intense. A region of anomalies that resembles the MCB can be seen in both the HPE and the non-HPE composites as the band of positive surface latent heat flux anomalies over the Arabian Sea, the Indian subcontinent, the Bay of Bengal, the southern regions of the Indochina Peninsula, and the southern half of the South China Sea. The difference is that the anomalies in the aforementioned regions are significantly higher in the HPE composites compared to the non-HPE composites, suggesting a much more developed MCB during HPEs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe composite anomalies from lag 0 to lag\u0026thinsp;+\u0026thinsp;5 for the indirect TC cases are shown in Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e. The indirect TC HPE composites (Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003ea to S6f) and the indirect TC non-HPE composites (Figure \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003eg to S6l) both show the TC continuing its northwestward trajectory and eventually landfalling over eastern China. The MCB in the HPE composites shows higher surface latent heat flux anomalies throughout all the time steps compared to the non-HPE composites, implying that the MCB is more defined during HPEs. The anomalies in the HPE composites also stay even after the TC has dissipated, unlike in the non-HPE composites, wherein the anomalies weaken and almost disappear, especially in the lag\u0026thinsp;+\u0026thinsp;4 and +\u0026thinsp;5 composites (Figures \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003ek and S6l).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003e3.2.3 SWM cases\u003c/h2\u003e\u003cp\u003eThe composite anomalies from lag \u0026minus;\u0026thinsp;5 to lag 0 for the SWM (no TC) cases are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e11\u003c/span\u003e. In the HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e11\u003c/span\u003ea to \u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e11\u003c/span\u003ef), a band of positive moisture flux anomalies similar to the anomalies seen in the previous HPE composites can be observed over the Arabian Sea, the Indian subcontinent, and the Bay of Bengal in the composites preceding lag 0. A region of positive moisture flux anomalies can also be found to the northeast of the Philippines, at around 20\u0026deg;N and 130\u0026deg;E in the lag \u0026minus;\u0026thinsp;5 composite. This anomaly travels northwestward and eventually ends up on the coast of eastern China in the lag \u0026minus;\u0026thinsp;3 and \u0026minus;\u0026thinsp;2 composites. A region of negative moisture flux anomalies also starts to form to the north of the Philippines in the lag \u0026minus;\u0026thinsp;2 composite, which gradually grows in size. By lag 0, this region of negative moisture flux anomalies encompasses a large region of the South China Sea, the tip of northern Philippines, and the entirety of Taiwan. The lack of positive anomalies and the presence of a large region of negative anomalies near the Philippines is expected, as the SWM HPE itself only occurs from lag 0 onwards. In the non-HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e11\u003c/span\u003eg to \u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e11\u003c/span\u003el), a similar band of positive anomalies can be observed over the Arabian Sea, the Indian subcontinent, and the Bay of Bengal in the composites preceding lag 0. The largest difference in the non-HPE composites compared to the HPE composites, however, is the presence of positive anomalies to the north of the Philippines from lag \u0026minus;\u0026thinsp;5 to lag \u0026minus;\u0026thinsp;1. This is simply due to the fact that the days leading up to the non-HPE are actually HPDs since the non-HPE only starts at lag 0. This transition can first be noticed in the lag \u0026minus;\u0026thinsp;2 composite, where a region of negative anomalies starts to form to the east of the Philippines, at around 20\u0026deg;N and 140\u0026deg;E. This region of negative anomalies gradually strengthens in magnitude and grows in size while slowly migrating westward. By lag 0, this region of negative anomalies is now situated to the northeast of the Philippines.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eNext, the composite anomalies from lag 0 to lag\u0026thinsp;+\u0026thinsp;5 for the SWM cases are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e12\u003c/span\u003e. In the HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e12\u003c/span\u003ea to \u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e12\u003c/span\u003ef), the large region of negative anomalies to the north of the Philippines slowly starts migrating northward. At the same time, the positive anomalies present over the Bay of Bengal and the Indochina Peninsula extend to the east, covering regions of the South China Sea and the southern half of the Philippines. By lag\u0026thinsp;+\u0026thinsp;2, the positive anomalies over the South China Sea have increased in magnitude and point more northwestward, towards Luzon Island. From lag\u0026thinsp;+\u0026thinsp;3 to lag\u0026thinsp;+\u0026thinsp;5, the positive anomalies seem to affect the entire western coast of Luzon Island. The large region of negative anomalies mentioned earlier has also disappeared in these time steps. In the non-HPE composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e12\u003c/span\u003eg to \u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e12\u003c/span\u003el), the region of the negative anomalies to the northeast of the Philippines continues to grow in size. More importantly, the negative anomalies seem to extend southwestward, towards the southern half of the South China Sea. From lag\u0026thinsp;+\u0026thinsp;2 to lag\u0026thinsp;+\u0026thinsp;5, almost the entire Philippines is covered by significant negative anomalies, which implies noticeably suppressed rainfall over the entire country during these days. Aside from the anomalies over the Philippines and the South China Sea, there seems to be no strong anomalies over the northern Indian Ocean region. This may suggest that the suppression of the SWM during SWM non-HPE cases is only localized to the WNP region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe surface latent heat flux composite anomalies from lag \u0026minus;\u0026thinsp;5 to lag 0 for the SWM cases are shown in Figure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e. In the HPE composites (Figure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003ea to S7f), positive surface latent heat flux anomalies can be observed over the Arabian Sea starting at the lag \u0026minus;\u0026thinsp;5 composite, which slightly weakens in the following time steps. Small positive anomalies can also be found over the South China Sea and the Philippines, which weaken until the lag \u0026minus;\u0026thinsp;1 composite, probably due to the formation of a large area of negative surface latent heat flux anomalies centered over the island of Taiwan. This area of negative anomalies reaches its peak around lag 0 (Figure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003ef). In the non-HPE composites (Figure \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003eg to S7l), positive anomalies are present over the Philippines until the lag \u0026minus;\u0026thinsp;1 composite. The positive anomalies are slowly replaced by a large region of negative anomalies coming from the east of the Philippines.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFinally, the composite anomalies from lag 0 to lag\u0026thinsp;+\u0026thinsp;5 are shown in Figure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e. In the HPE composites (Figure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003ea to S8f), the large region of negative anomalies to the north of the Philippines slowly starts to disappear at the same time as more positive anomalies start to affect the country. In the lag\u0026thinsp;+\u0026thinsp;1 composite, only the southern half of the Philippines is affected by positive surface latent heat flux anomalies. By the lag\u0026thinsp;+\u0026thinsp;3 composite, the positive anomalies now affect the entire western coast of Luzon. This northward migration of these anomalies during the days succeeding lag 0 can also be observed in the moisture flux anomalies as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig18\" class=\"InternalRef\"\u003e12\u003c/span\u003e. In the non-HPE composites (Figure \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003eg to S8l), the large region of negative anomalies to the east of the Philippines slowly migrated westward. By the lag\u0026thinsp;+\u0026thinsp;2 composite, the entire Philippines is covered in negative anomalies, as well as the southern half of the South China Sea. Similar to the moisture flux anomalies for this case, the suppression of the surface latent heat flux seems to only be localized over the WNP region and does not reach the Bay of Bengal and the Arabian Sea.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe composites presented in this section have provided evidence that the MCB is a very important factor in the occurrence of HPEs. The formation of the MCB during the presence of TCs also seems to be reliant on the moisture flux and surface latent heat flux anomalies in certain regions of the northern Indian Ocean, mainly the Arabian Sea and the Bay of Bengal. Whenever these regions experience high anomalies, TCs that pass near the Philippines enhance the SWM, which creates an MCB that extends from the Arabian Sea to the TC. The MCB traverses several regions of land, such as the Indian subcontinent, the Malay Peninsula, the Indochina Peninsula, and the Philippines, possibly producing heavy rainfall over these areas. During cases wherein the Arabian Sea and the Bay of Bengal do not feature strong positive anomalies, a TC may still be able to enhance the SWM. However, based on the presented composites, the moisture content of the enhanced SWM is significantly lower due to the lack of an efficient moisture transport from the Indian Ocean. Additionally, this enhancement is only localized to the WNP, mainly the South China Sea and the Philippines. This leads to less rainfall over the Philippines.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.3 The MCB and the influence of the BSISO\u003c/h2\u003e\u003cp\u003eIt can be noticed from the previous section that there are certain regions that experience higher vertically integrated moisture flux and surface latent heat flux anomalies during HPEs compared to non-HPEs. Specifically, these regions are the Arabian Sea, the Bay of Bengal, and the southern half of the South China Sea. Moisture flux and surface latent heat flux anomalies can be found over the Arabian Sea and Bay of Bengal before and after the passage of the TC in the WNP, implying that these anomalies are independent of the TC. Another observation from the composites is that the anomalies over the southern half of the South China Sea appear once a TC approaches the Philippines. These anomalies, however, are significantly stronger during HPEs, when there are also positive anomalies over the Arabian Sea and the Bay of Bengal, compared to non-HPEs. The formation of the MCB, which leads to the HPEs over the Philippines, seems to be dependent on the moisture flux and surface latent heat flux over the Arabian Sea, the Bay of Bengal, and the southern half of the South China Sea. This section will attempt to determine the daily moisture flux fluctuations in these regions and its effect on the occurrence of HPEs over the Philippines.\u003c/p\u003e\u003cdiv id=\"Sec14\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 The MCB region\u003c/h2\u003e\u003cp\u003eThe moisture flux anomalies over these three regions, which will now be referred to as R1, R2, and R3 (as seen in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), were averaged to obtain a singular daily value for each region. To determine if the anomalies over these three regions are related to the occurrence of HPDs over the Philippines, the HPDs that simultaneously feature positive moisture flux anomalies in each region were determined. The results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. For both direct and indirect TC HPDs, R3 has the highest percentages, with 71.7% of direct TC HPDs and 78.3% of indirect TC HPDs coinciding with days that have positive moisture flux anomalies over R3. This is expected as this region is the closest to the WNP; therefore, the closest to the TCs, and more prone to experiencing positive anomalies whenever a TC passes nearby. The percentage differences between R1 and R2 during direct and indirect TC HPDs are within one percent of each other, possibly implying that the anomalies in R1 and R2 both have a somewhat equal relationship with HPDs over the Philippines. Interestingly, the trend of the percentages is flipped for the SWM HPDs. Here, 62.8% of SWM HPDs coincided with days that have positive moisture flux anomalies over R1. This is higher than the R2 and R3 percentages, which are 59.8% and 58.2%, respectively. The percentages for the SWM HPDs may suggest that these kinds of HPDs are related to the overall strength of the Asian monsoon system as a whole, instead of just the SWM over the South China Sea and the Philippines. This is in line with the SWM HPE composites in Fig.\u0026nbsp;\u003cspan refid=\"Fig17\" class=\"InternalRef\"\u003e11\u003c/span\u003e, wherein the moisture flux anomalies start from the northern Indian Ocean prior to the HPE. These anomalies then extend over the South China Sea and the Philippines during the HPE.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of direct TC, indirect TC, and SWM HPDs that simultaneously featured positive moisture flux anomalies over R1, R2, or R3. Values inside parentheses are the percentages of that specific HPD type with anomalies over each region.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eType of HPD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDays with positive R1 anomalies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDays with positive R2 anomalies\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDays with positive R3 anomalies\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDirect TC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e121 (51.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e123 (52.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e167 (71.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndirect TC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e309 (61.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e308 (60.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e396 (78.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSWM\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e512 (62.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e487 (59.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e474 (58.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTotal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e942 (60.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e918 (59.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1037 (66.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eNext, the correlation between the number of days that feature positive moisture flux anomalies over each of the three regions and the number of HPDs experienced by the Philippines every year was calculated. The Pearson correlation coefficients for HPDs and R1, R2, and R3 positive anomaly days are 0.82, 0.81, and 0.87, respectively. All three values show a very strong positive correlation and are all statistically significant at the 95% confidence level. The very high R3 correlation coefficient is expected, as this is the region closest to the Philippines; therefore, the more days with positive anomalies in this region, the more HPDs the Philippines experiences. An interesting finding is that the correlation coefficients for R1 and R2 are nearly as high as R3, despite being further away from the Philippines. This suggests that all three regions are interconnected and the occurrence of HPDs in the Philippines is not only due to the local enhancement of the SWM, but is also due to a large-scale strengthening of the entire Asian monsoon.\u003c/p\u003e\u003cp\u003eFor a deeper look into the daily moisture flux variations, the years 2005, 2012, and 2021 were selected, and a time series of the daily moisture flux anomalies for all three regions was created. All three of these years had TCs that enhanced the SWM and brought heavy rainfall over the western coast of the Philippines. For the year 2005, it was Typhoon Matsa. For the year 2012, the TCs were Saola and Haikui. For the year 2021, the TCs were In-fa and Cempaka. The moisture flux anomaly time series for each of the three years is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e13\u003c/span\u003e. Included in the figure are the TC days, depicted as the black stars, as well as the HPDs, depicted as the colored dots. Red dots represent direct TC HPDs, blue dots represent indirect TC HPDs, while yellow dots indicate SWM HPDs. At first glance, the moisture flux anomalies in all three regions are observed to drastically vary in magnitude throughout the season. Episodes of positive and negative moisture flux anomalies, which last for several weeks, can be seen in all three years for all three regions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eR1, which is the Arabian Sea region, is the furthest away from the WNP. Because of this, it can be assumed that TCs in the WNP are unlikely to have a large effect on the anomalies over this region. This means that the peaks and valleys in the moisture flux anomalies of R1 are most likely not related to WNP TC activity. However, anomalies in R2 and R3 do somewhat follow the overall trend of the R1 anomalies. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows the Pearson correlation coefficients for R1 and R2, R2 and R3, and R1 and R3. It is no surprise that the regions that are adjacent to each other (R1 and R2, R2 and R3) have a higher Pearson correlation coefficient than the regions that are far apart (R1 and R3). Although, aside from the year 2012, the Pearson correlation coefficient for R1 and R3 is still statistically significant at the 95% confidence level. While the moisture flux anomalies of all three regions seem to be somewhat related, it seems that another factor that modulates the moisture flux anomalies over R3 is the presence of TCs in the WNP. This behavior is seen as the high, localized spikes in the moisture flux anomalies in R3 during the presence of TCs. For the 2005 case (Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e13\u003c/span\u003ea), spikes in R3 anomalies can be observed during the TC days of early August, as well as during the TC days of mid and late September. For the 2012 case (Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e13\u003c/span\u003eb), spikes in R3 anomalies can be observed during the TC days of late July, early August, mid-September, and late September. For the 2021 case (Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e13\u003c/span\u003ec), spikes in R3 anomalies can be observed during the TC days of mid-July, early August, and early September. This makes sense as R3 is the region closest to the WNP. However, the positive anomalies in R3 whenever a TC is near the Philippines seem to be greater in magnitude if R1 and R2 are also experiencing positive anomalies. For example, in the year 2005 (Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e13\u003c/span\u003ea), all three regions experienced an increase in moisture flux anomalies during the second half of July. After this spike of anomalies, the R2 and R3 anomalies eventually decreased. R1 followed this decreasing trend at the start of August; however, the presence of Typhoon Matsa and Tropical Storm Sanvu caused the R3 anomalies to spike once again, which produced three consecutive indirect TC HPDs. Once the two TCs disappeared, the R3 anomalies quickly dropped to the level of the R1 and R2 anomalies, which have been steadily decreasing since the start of August. Anomalies over all three regions continue to drop until late August. The presence of Typhoon Talim during the final days of August caused an uptick in the moisture flux values; however, these anomalies are fairly low and were only able to produce one HPD since R1 and R2 were experiencing negative anomalies. R1 and R2 anomalies peaked once again in mid-September, leading to sudden peaks in R3 anomalies during the presence of TCs. In the 2012 graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e13\u003c/span\u003eb), the 2012 heavy rainfall event can be seen as the consecutive TC days and HPDs spanning from late July until early August. Compared to 2005, R1 during this year didn\u0026rsquo;t experience any major anomalies until mid-September. However, it is worth noting that R1 and R2 displayed positive anomalies during the 2012 heavy rainfall event. The presence of Typhoons Saola and Haikui caused high peaks in R3 anomalies, producing several HPDs. Further into the season, two TCs (Kai-tak and Tembin) appeared during mid-late September, producing several consecutive TC days. However, these TC days did not produce as many HPDs compared to Saola and Haikui. This may be due to the dip in R1 and R2 anomalies during this time, which also caused the R3 anomalies to dip. The presence of Typhoon Sanba during mid-September was able to cause a spike in R3 anomalies; however, this spike was lower compared to the spikes seen with Saola and Haikui, which may be due to the low anomalies found in R1 and R2. In the 2021 graph (Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e13\u003c/span\u003ec), a very large increase in moisture flux anomalies can be observed in R1 during mid-July. This spike in anomalies seems to be independent of TC activity or the anomalies in R2 and R3. However, this increase in R1 anomalies did allow R2 and R3 to significantly increase during the lifespan of Typhoon In-fa, producing several consecutive HPDs. The anomalies in all three regions suddenly dropped in early August and continued to display negative anomalies throughout the rest of the month. The R1 anomalies slowly increased until early September, allowing R2 and R3 to significantly increase in the presence of Tropical Storm Conson.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePearson correlation coefficients for the moisture flux anomalies over the regions R1 and R2, R2 and R3, and R1 and R3 for the years 2005, 2012, and 2021. Bold values denote values that are statistically significant at the 95% confidence level.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eR1 and R2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eR2 and R3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR1 and R3\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.797\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.554\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.504\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.603\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.488\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.148\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e\u003cb\u003e0.634\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e\u003cb\u003e0.739\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e\u003cb\u003e0.628\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eIt can be observed from Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e13\u003c/span\u003e that the fluctuations in moisture flux in all three regions are seemingly in phase with one another. Furthermore, due to the distance between R1 and the WNP, anomalies over R1 seem to be independent of TC activity over the WNP. This may suggest that any moisture flux fluctuations experienced over the Arabian Sea are due to a completely different mechanism. TC activity does seem to affect the anomalies over R3, and to a lesser extent, R2. Local peaks in R3 anomalies, and sometimes R2 anomalies, seem to coincide with the existence of TCs over the WNP. This means that TCs can locally enhance the SWM over the South China Sea. However, the magnitude of these local peaks is still dependent on the overall trend of the moisture flux anomalies of all three regions. For example, if all three regions show positive moisture flux anomalies and a TC passes through the northeast of the Philippines, the chances of an HPD, especially an indirect TC HPD, occurring are higher than if all three regions show negative moisture flux anomalies. This is in line with Bagtasa (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), wherein it was stated that not all TCs to the northeast of the Philippines produce heavy rainfall over the western coast of Luzon. The conditions in all three regions must be right for a TC to produce a fully-formed MCB.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 The influence of the BSISO\u003c/h2\u003e\u003cp\u003eA possible explanation for the moisture flux variations during HPEs may be the BSISO (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Guo et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). From the three years discussed in this section, days that featured a moisture flux anomaly value exceeding 200 kg m\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e averaged over the three regions were identified. Out of the 35 identified days, 26 occurred during either the 5th, 6th, or 7th phase of the BSISO. Due to the propagation characteristics of the BSISO, enhanced convection is present over the Philippines during these phases, which may be the reason for the extreme rainfall. The remaining nine days were identified to occur during phase 8 of the BSISO, wherein the area of enhanced convection is situated to the northeast of the Philippines (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Kikuchi, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For a deeper analysis, the moisture flux anomalies over all three regions were averaged for each BSISO phase from 1981 to 2022 (BSISO index only goes back to 1981) and is shown in Figure S9. Interestingly enough, the moisture flux anomalies for all three regions appear to peak during phase 6 despite the BSISO convection only being near the vicinity of R3 during this phase. The positive outgoing longwave radiation (OLR) anomalies observed over the tropical northern Indian Ocean during phase 6, as reported by Lee et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and Kikuchi (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), imply suppressed convection over regions R1 and R2. Similarly, during phases 2 and 3, the BSISO convection is situated over the tropical northern Indian Ocean, yet R1 and R2 experience negative moisture flux anomalies. This discrepancy may imply that the moisture flux and OLR anomalies accompanying the convective region of the BSISO are not directly related. This may explain why the moisture flux anomalies in all three regions are seemingly in phase with one another (observed in Fig.\u0026nbsp;\u003cspan refid=\"Fig21\" class=\"InternalRef\"\u003e13\u003c/span\u003e), as the moisture flux anomalies do not follow the eastward propagation pattern of the BSISO. Instead, the moisture flux anomalies over the three regions seem to be dependent on the horizontal wind anomalies that accompany the BSISO convection. For example, during phases 2 and 3, wherein the BSISO convection is over the tropical northern Indian Ocean, easterly horizontal wind anomalies are present over R1 and R2, which suppresses the westerly monsoon flow and moisture transport. Similarly, during phases 6 and 7, the convection is over the Philippines, but westerly horizontal wind anomalies are present in all three regions. This enhances the westerly monsoon flow and the moisture transport.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eTo further investigate the relationship between HPDs and the BSISO, all the direct TC, indirect TC, and SWM HPDs were plotted into their respective phase space diagrams and are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig23\" class=\"InternalRef\"\u003e14\u003c/span\u003e. For this section, significant BSISO HPDs are defined as HPDs with a BSISO amplitude greater than 1. The phase with the highest percentage of significant BSISO HPDs actually differs depending on the type of HPD. For direct TC HPDs, the peak is during phase 5. For indirect TC HPDs, the peak is during phase 7. Finally, for SWM HPDs, the peaks are phases 5 and 7; however, the percentages for phases 4 and 6 are not that far behind. This implies that the occurrence of SWM HPDs is more spread out during phases 4 to 6, unlike in direct and indirect TC HPDs, where there are evident peaks. The difference in peaks between direct and indirect TC HPDs may be attributed to the changes in the horizontal wind anomalies and the location of the BSISO convection during each phase. Moon et al. (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) and Zhang et al. (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) stated that TC genesis points typically follow the BSISO convection when it\u0026rsquo;s moving along the WNP. A similar phenomenon was observed by Kikuchi and Wang (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), but with cyclones over the northern Indian Ocean. The cause for this is the midtropospheric vertical motion accompanying the BSISO convection. This vertical motion is the biggest factor controlling TC genesis in the region (Moon et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). During phase 5 of the BSISO, the area of convection is situated over the South China Sea and the Philippines; therefore, any TCs forming in this region will most likely be classified as a direct TC due to their proximity to the Philippines. On the other hand, during phase 7 of the BSISO, the area of convection is now situated more to the east of the Philippines. This leads to TCs forming much deeper into the Pacific Ocean compared to TCs that formed during phase 5. Additionally, Wu et al. (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) discovered changes in the location of the 5870 geopotential height at 500 hPa during the different BSISO phases. The 5870 gpm contour line typically marks the edge of the western North Pacific subtropical high (WNPSH) and has a significant impact on the tracks of TCs. During phase 5, a significant westward extension in the 5870 gpm contour line occurs, causing the WNPSH to possibly steer TCs towards the Philippines, leading to more direct TC HPDs. During phase 7, there is still a westward extension, but not as drastic compared to phase 5. This may allow some TCs during this phase to recurve northward instead of traversing the Philippines, possibly leading to more indirect TC HPDs.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOverall, the BSISO seems to have an effect on the MCB since it modulates the two key ingredients for MCB formation: the strong monsoon westerlies over the tropical northern Indian Ocean and South China Sea, and the occurrence of TCs over the WNP. Guo et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) examined the relationship between the occurrence and distribution of MCB and the BSISO. They found more frequent formation of the MCB over the WNP during the northward propagation of the convective envelope of the BSISO to the subtropics, particularly between phases 7 and 8. Significant positive moisture flux anomalies over R1, R2, and R3 can be observed during phases 5, 6, and 7, which imply the presence of strong monsoon westerlies, which is the first ingredient for the MCB (Figure S9). At the same time, TC formation is more likely in these phases since the BSISO convection, which creates instability and vertical motion anomalies, is now situated over the WNP. With the presence of the strong monsoon westerlies and the TC, the MCB will now most likely form and cause extreme rainfall in various regions, especially over the western coast of the Philippines.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"4. Summary and Conclusions","content":"\u003cp\u003eThe HPDs from July to September for the period 1961 to 2022 were identified and separated into direct TC HPDs, indirect TC HPDs, and SWM HPDs, depending on the location of the TC present. Direct TC, indirect TC, and SWM HPDs account for 15.0%, 32.6%, and 52.4% of all HPDs in the analysis period, respectively. The average distribution of the HPDs within a JAS season is as follows: 33.6% for July, 39.9% for August, and 27.5% for September. This distribution closely resembles the evolution of the SWM over the Philippines. The SWM fully establishes itself during July, reaches its peak during August, and gradually weakens in September. For direct TC HPDs, a correlation between direct TC HPD occurrence and the latitude of the station was found. This is simply because TCs during the JAS season tend to move north or northeast of the Philippines, thus affecting the northern stations more. For indirect TC HPDs, the occurrence seems to be related to the local geography surrounding the station, such as high mountains to the east and northeast of the station. This is due to the orographic lifting effect, which forces the warm, moist southwesterlies brought by the TC-enhanced SWM upwards, leading to heavy rainfall. For SWM HPDs, their occurrence is simply dependent on the occurrence of the TC-related HPDs. More TC-related HPDs generally mean fewer SWM HPDs.\u003c/p\u003e\u003cp\u003eThe mean TC position during direct TC and indirect TC HPDs was identified. The mean direct TC position is over Cagayan province, which is located on the northern edge of Luzon Island. This is expected since JAS TCs affect the northern Philippines more. The mean indirect TC position is situated several hundred kilometers northeast of Luzon. This TC position is in line with findings by Cayanan et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Bagtasa (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), which stated that the western coast of Luzon experiences heavy rainfall when a TC to the northeast is present.\u003c/p\u003e\u003cp\u003eFor both direct and indirect TC HPDs, the majority of the TCs that produce HPDs are situated in the NE quadrant of the station. This is expected since for both direct and indirect TC HPDs, a northeast TC position means that the winds experienced by the station come from the southwest. Southwesterly winds can produce more rainfall via the orographic lifting effect since high mountains are present to the east and northeast of most of the stations. However, the quadrant with the second highest HPD percentage is different for both direct and indirect TC HPDs. The quadrant with the second highest HPD percentage for the direct TC HPDs is the SE quadrant. This is because TCs usually travel northwestward, which means that TCs that will directly pass a station will appear on that station\u0026rsquo;s SE quadrant. Meanwhile, the quadrant with the second highest HPD percentage for the indirect TC HPDs is the NE quadrant. This follows the study by Cayanan et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), which stated that TCs to the northwest of Luzon Island are still able to bring extreme rainfall over the western coast of Luzon.\u003c/p\u003e\u003cp\u003eDetermining a relationship between TC distance and rainfall amount during HPDs was attempted, but a clear correlation between the two was not found. The same can be said for TC intensity and rainfall amount. This suggests that the characteristics of the TC, such as position and intensity, are not the only factors in the modulation of the rainfall over the western coast of Luzon during enhanced SWM cases. This was verified in the lag composites comparing HPEs and non-HPEs. The HPE composites featured very noticeable moisture flux and surface latent heat flux anomalies over the Arabian Sea, the Bay of Bengal, and the southern half of the South China Sea, which were not present or not as evident in the non-HPE composites. The anomalies over the Arabian Sea and the Bay of Bengal also seem to be present before and after the lifespan of the TC, suggesting that these anomalies are independent of the TC. A schematic diagram was created based on the results of the lag composites (Fig.\u0026nbsp;\u003cspan refid=\"Fig24\" class=\"InternalRef\"\u003e15\u003c/span\u003e). The region of moisture flux anomalies over the tropical northern Indian Ocean and the WNP, as seen by the blue shaded areas, is significantly larger during the TC-related HPEs compared to the TC-related non-HPEs. This implies that the MCB is only present, or more pronounced, during HPEs. Additionally, SWM HPEs also feature a long band of positive moisture flux over the tropical northern Indian Ocean and the South China Sea, similar to that of the MCB. This band grazes the western coast of the Philippines, causing heavy rainfall over these regions during SWM HPEs. For the SWM non-HPEs, a large region of negative moisture flux anomalies covers the entirety of the Philippines, the Philippine Sea, and the southern half of the South China Sea. The lower moisture content of the SWM during these days is responsible for the suppressed rainfall over the western coast of the Philippines.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe moisture flux anomalies over the Arabian Sea, the Bay of Bengal, and the southern half of the South China Sea, during three specific years, were analyzed to find a relationship between the anomalies and the occurrence of TC-related HPEs. The time series revealed that all three regions featured daily variations in moisture flux anomalies. The anomalies in the three regions seem to be in phase with one another as most of the time, all three regions either showed positive or negative anomalies. It was also observed that the R1 anomalies are somewhat independent of TC activity over the WNP, while R3 is noticeably dependent. Spikes in moisture flux anomalies over R3 can be observed during TC days; however, these spikes still follow the overall trend of R1 and R2. This means that spikes in moisture flux in R3 when R1 and R2 exhibit positive anomalies are more likely to cause HPDs than when R1 and R2 exhibit negative anomalies.\u003c/p\u003e\u003cp\u003eLastly, the role of the BSISO in the formation of the MCB was investigated. It was determined that the horizontal wind anomalies accompanying the propagation of the BSISO convection are able to modulate the moisture flux over R1, R2, and R3. The moisture flux over these regions seems to peak during phase 6 when the BSISO convection is over the Philippines. Additionally, TCs are more likely to form during phases 5 to 7 as the BSISO convection creates a region over the WNP of very favorable conditions for TC genesis. The higher frequency of TCs and the strong moisture flux over the northern Indian Ocean and the South China Sea can produce the MCB, which causes HPDs/HPEs.\u003c/p\u003e\u003cp\u003eKudo et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) and Fujiwara et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) only defined the MCB starting from the Bay of Bengal, stretching over the Indochina Peninsula, and then over the WNP and the Philippines. Future studies regarding the MCB could include the Arabian Sea, as the composites presented in this study revealed that the moisture flux anomalies of the MCB also extend to this region. Further studies regarding the BSISO could also possibly include moisture flux composites in addition to the typical horizontal wind and OLR composites. The evolution of moisture transport during the life cycle of the BSISO may provide further insights into rainfall patterns and help deepen our understanding of monsoon behavior during each BSISO phase.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\u003c/p\u003e\u003cp\u003e\u003ch2\u003eDeclaration of Interests\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have influenced the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eAll authors contributed to the study conception and design. Implementation of the methodology and formal analysis were done by A.A.L. Bathan, L.M.P. Olaguera, F.A.T. Cruz, J.R.T. Villarin, J.T. Maquiling, M.O.L. Cambaliza, J.A. Manalo, and J. Matsumoto supervised, reviewed, and edited the manuscript. The first draft of the manuscript was written by A.A.L. Bathan, L.M.P. Olaguera. All authors commented on previous versions of the manuscript, read, and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eA.A.L. Bathan, L.M.P. Olaguera, F.A.T. Cruz, and J.R.T. Villarin were supported by the High-definition Clean Energy, Climate, and Weather Forecasts for the Philippines project of the Manila Observatory. Part of this study was supported by Grant-in-Aid for Scientific Research 22H04938; PI Kei Yoshimura of the University of Tokyo and No. 24K00172; PI Yoshiyuki Kajikawa of Kobe University.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe rainfall data set from PAGASA may be requested through the following link: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.pagasa.dost.gov.ph/climate/climate-data\u003c/span\u003e\u003cspan address=\"https://www.pagasa.dost.gov.ph/climate/climate-data\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The reanalysis data from ERA5 are publicly available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview\u003c/span\u003e\u003cspan address=\"https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBagtasa G (2019) Enhancement of Summer Monsoon Rainfall by Tropical Cyclones in Northwestern Philippines. J Meteor Soc Japan. 97: 967\u0026minus;976. https://doi.org/10.2151/jmsj.2019-052 \u003c/li\u003e\n\u003cli\u003eBagtasa G (2023) Characterization of the 2012 and 2013 Metro Manila \u0026quot;Enhanced Habagat\u0026quot; Heavy Rainfall Events. Philipp J Sci. 152: 123\u0026ndash;135. https://doi.org/10.56899/152.S1.10\u003c/li\u003e\n\u003cli\u003eBathan AAL, Olaguera LMP, Cruz FAT, Villarin JRT, Maquiling JT, Cambaliza MOL, Manalo JA, Matsumoto J (2025) Influence of tropical cyclones, southwest monsoon, and tropical cyclone-enhanced southwest monsoon on rainfall variability over the western coast of the Philippines. Atmos Res. 326. https://doi.org/10.1016/j.atmosres.2025.108273\u003c/li\u003e\n\u003cli\u003eCayanan E, Chen T, Argete J, Yen M, Nilo P (2011) The effect of tropical cyclones on southwest monsoon rainfall in the Philippines. J Meteor Soc Japan. 89A, 123\u0026ndash;139. https://doi.org/10.2151/jmsj.2011-A08\u003c/li\u003e\n\u003cli\u003eCorporal-Lodangco I, Leslie L (2017) Climatology of Philippine tropical cyclone activity: 1945-2011. Int J Climatol. 37: 3525\u0026ndash;3539. https://doi.org/10.1002/joc.4931 \u003c/li\u003e\n\u003cli\u003eCruz F, Narisma G, Villafuerte II M, Chua KU, Olaguera LM (2013) A climatological analysis of the southwest monsoon rainfall in the Philippines. Atmos Res. 122:609\u0026ndash;616. https://doi.org/10.1016/j.atmosres.2012.06.010. \u003c/li\u003e\n\u003cli\u003eDare R, Davidson N, Mcbride J (2012) Tropical cyclone contribution to rainfall over Australia. Mon Wea Rev. 140: 3606\u0026ndash;3619. https://doi.org/10.1175/MWR-D-11-00340.1\u003c/li\u003e\n\u003cli\u003eDas PK (1986) Monsoons. Fifth WMO Lecture. World Meteorological Organization. Geneva\u003c/li\u003e\n\u003cli\u003eEfron B, Tibshirani RJ (1994) An Introduction to the Bootstrap. Philadelphia, PA: Chapman \u0026amp; Hall/CRC Press. 436 pp\u003c/li\u003e\n\u003cli\u003eFujiwara K, Kawamura R, Hirata H, Kawano T, Kato M, Shinoda T (2017) A positive feedback process between tropical cyclone intensity and the moisture conveyor belt assessed with Lagrangian diagnostics. J Geophys Res Atmos. 122:12,502\u0026ndash;12,521. https://doi.org/10.1002/ 2017JD027557 \u003c/li\u003e\n\u003cli\u003eGuo X, Zhao N, Kikuchi K, Nasuno T, Nakano M, Annamalai H (2021) Atmospheric rivers over the Indo-Pacific and its associations with the boreal summer intraseasonal oscillation. J Clim. 34: 9711\u0026ndash;9728. https://doi.org/10.1175/JCLI-D-21-0290.1\u003c/li\u003e\n\u003cli\u003eHalpern D, Woiceshyn PM (1999) Onset of the Somali Jet in the Arabian Sea during June 1997. J Geophys Res. 104(C8):18041\u0026ndash;18046. https://doi.org/10.1029/1999JC900141\u003c/li\u003e\n\u003cli\u003eHegde AK, Kawamura R, Kawano T (2016) Evidence for the significant role of sea surface temperature distributions over remote tropical oceans in tropical cyclone intensity. Clim. Dyn. 47: 623\u0026ndash;635. https://doi.org/10.1007/s00382-015-2859-.8\u003c/li\u003e\n\u003cli\u003eHersbach H, Bell B, Berrisford P, Hirahara S, Hor\u0026aacute;nyi A, Mu\u0026ntilde;oz‐Sabater J, Nicolas J, Peubey C, Radu R, Schepers D, Simmons A (2020) The ERA5 global reanalysis. Q J R Meteorol Soc. 2020; 146:1999\u0026ndash;2049. https://doi.org/10.1002/qj.3803\u003c/li\u003e\n\u003cli\u003eJTWC (2017) Frequently Asked Questions. Joint Typhoon Warning Center. https://www.metoc.navy.mil/jtwc/jtwc.html?faq. Accessed 02 April 2025\u003c/li\u003e\n\u003cli\u003eKhouakhi A, Villarini G, Vecchi GA (2017) Contribution of tropical cyclones to rainfall at the global scale. J Clim. 30:359\u0026ndash;372. https://doi.org/10.1175/JCLI-D-16-0298.1\u003c/li\u003e\n\u003cli\u003eKintanar RL (1984) Climate of the Philippines. Quezon City, Philippines: Philippine Atmospheric, Geophysical and Astronomical Services Administration.\u003c/li\u003e\n\u003cli\u003eKikuchi K (2021) The Boreal Summer Intraseasonal Oscillation (BSISO): A Review. J Meteor Soc Japan. 99. https://doi.org/10.2151/jmsj.2021-045 \u003c/li\u003e\n\u003cli\u003eKikuchi K, Wang B (2010) Formation of tropical cyclones in the Northern Indian Ocean associated with two types of tropical intraseasonal oscillation modes. J Meteor Soc Jpn. 88: 475\u0026ndash;496. https://doi.org/10.2151/jmsj.2010-313\u003c/li\u003e\n\u003cli\u003eKubota H, Kosaka Y, Xie S-P (2016) A 117-year long index of the Pacific-Japan pattern with application to interdecadal variability. Int J Climatol. 36: 1575\u0026ndash;1589. https://doi.org/10.1002/joc.4441.\u003c/li\u003e\n\u003cli\u003eKubota H, Shirooka R, Matsumoto J, Cayanan E, Hilario F (2017) Tropical cyclone influence on the long-term variability of Philippine summer monsoon onset. Prog Earth Planet Sci. 4, 27. https://doi.org/10.1186/s40645-017-0138-5.\u003c/li\u003e\n\u003cli\u003eKubota H, Wang B (2009) How much do tropical cyclones affect seasonal and interannual rainfall variability over the Western North Pacific? J Clim. 22: 5495\u0026ndash;5510. https://doi.org/10.1175/2009JCLI2646.1 \u003c/li\u003e\n\u003cli\u003eKudo T, Kawamura R, Hirata H, Ichiyanagi K, Tanoue M, Yoshimura K (2014) Large-scale vapor transport of remotely evaporated seawater by a Rossby wave response to typhoon forcing during the Baiu/Meiyu season as revealed by the JRA-55 reanalysis. J Geophys Res Atmos. 119: 8825\u0026ndash;8838. https://doi.org/10.1002/2014JD021999\u003c/li\u003e\n\u003cli\u003eLagmay AM, Bagtasa G, Crisologo I, Racoma BA, David C (2015) Volcanoes magnify Metro Manila\u0026apos;s southwest monsoon rains and lethal floods. Front Earth Sci. 2. https://doi.org/10.3389/feart.2014.00036 \u003c/li\u003e\n\u003cli\u003eLau KM, Zhou YP, Wu HT (2008) Have tropical cyclones been feeding more extreme rainfall? J Geophys Res Atmos. 113: D23113. https://doi.org/10.1029/2008JD00996 \u003c/li\u003e\n\u003cli\u003eLee JY, Wang B, Wheeler MC, Fu X, Waliser D, Kang (2013) Real-time multivariate indices for the boreal summer intraseasonal oscillation over the Asian summer monsoon region. Clim Dyn. 40: 493\u0026ndash;509. https://doi.org/10.1007/s00382-012-1544-4\u003c/li\u003e\n\u003cli\u003eLiu S, Xu J, Tu S, Zheng M, Chen Z (2023) Relationship between South China Sea Summer Monsoon and Western North Pacific Tropical Cyclones Linkages with the Interaction of Indo-Pacific Pattern. Atmosphere. 14: 645. https://doi.org/10.3390/atmos14040645\u003c/li\u003e\n\u003cli\u003eLyon B, Camargo S (2009) The seasonally-varying influence of ENSO on rainfall and tropical cyclone activity in the Philippines. Clim Dyn. 32: 125\u0026ndash;141. https://doi.org/10.1007/s00382-008-0380-z. \u003c/li\u003e\n\u003cli\u003eMatsumoto J, Olaguera LM, Nguyen\u0026ndash;Le D, Kubota H, Villafuerte II M (2020) Climatological seasonal changes of wind and rainfall in the Philippines. Int J Climatol. 40. https://doi.org/10.1002/joc.6492. \u003c/li\u003e\n\u003cli\u003eMoon J, Wang B, Lee S, Ha K (2018) An intraseasonal Genesis Potential Index for tropical cyclones during northern hemisphere summer. J Clim. 31: 9055\u0026ndash;9071. https://doi.org/10.1175/JCLI-D-18-0515.1\u003c/li\u003e\n\u003cli\u003eMurakami T, Matsumoto J (1994) Summer monsoon over the Asian Continent and western North Pacific. J Meteor Soc Jpn. 72: 719\u0026ndash;745. https://doi.org/10.2151/jmsj1965.72.5_719. \u003c/li\u003e\n\u003cli\u003eNogueira RC, Keim BD (2010) Annual volume and area variations in tropical cyclone rainfall over the Eastern United States. J Clim. 23: 4363\u0026ndash;4374, https://doi.org/10.1175/2010JCLI3443.1\u003c/li\u003e\n\u003cli\u003eOlaguera LMP, Cruz F, Dado JM, Villarin JR (2022a) Complexities of extreme rainfall in the Philippines. In: Unnikrishnan, A., Tangang, F., Durrheim, R.J. (eds) Extreme Natural Events. Springer, Singapore. https://doi.org/10.1007/978-981-19-2511-5_5 \u003c/li\u003e\n\u003cli\u003eOlaguera LMP, Manalo JA, Matsumoto J (2022b) Influence of boreal summer intraseasonal oscillation on rainfall extremes in the Philippines. Int J Climatol. 42: 4656\u0026ndash;4668.\u003c/li\u003e\n\u003cli\u003eOlaguera LM, Manalo J (2024) Objective determination of the withdrawal dates of the southwest monsoon (Habagat) season of the Philippines using cumulative rainfall. Philipp J Sci. 153(5):1579\u0026ndash;1591.\u003c/li\u003e\n\u003cli\u003ePAGASA (2018) Climate of the Philippines. Philippine Atmospheric, Geophysical and Astronomical Services Administration. https://www.pagasa.dost.gov.ph/information/climate-philippines. Accessed 25 March 2025\u003c/li\u003e\n\u003cli\u003ePAGASA (2024) Climate of the Philippines. Philippine Atmospheric, Geophysical and Astronomical Services Administration. https://www.pagasa.dost.gov.ph/information/about-tropical-cyclone. Accessed 13 March 2025 \u003c/li\u003e\n\u003cli\u003eP\u0026eacute;rez-Alarc\u0026oacute;n A, Sor\u0026iacute; R, Fern\u0026aacute;ndez-Alvarez JC, Nieto R, Gimeno L (2023) Moisture source for the precipitation of tropical cyclones over the Pacific Ocean through a Lagrangian approach. J Clim. 36: 1059\u0026ndash;1083. https://doi.org/10.1175/JCLI-D-22-0287.1\u003c/li\u003e\n\u003cli\u003ePrat OP, Nelson BR, (2013) Mapping the world\u0026apos;s tropical cyclone rainfall contribution over land using the TRMM Multi-satellite Precipitation Analysis. Water Resour Res. 49: 7236\u0026ndash;7254. https://doi.org/:10.1002/wrcr.20527 \u003c/li\u003e\n\u003cli\u003eTurney S (2024) Pearson Correlation Coefficient (r) | Guide \u0026amp; Examples. Scribbr. https://www.scribbr.com/statistics/pearson-correlation-coefficient/. Accessed 14 April 2025\u003c/li\u003e\n\u003cli\u003eWang B, Ding Q, Fu X, Kang I, Jin K, Shukla J, Doblas-Reyes F (2005) Fundamental challenge in simulation and prediction of summer monsoon rainfall, Geophys Res Lett. 32, L15711. https://doi.org/10.1029/2005GL022734. \u003c/li\u003e\n\u003cli\u003eWu P, Ling Z, He H (2023) The effects of intraseasonal oscillations on landfalling tropical cyclones in the Philippines during the boreal summer. Front Earth Sci. 11:1106291. https://doi.org/10.3389/feart.2023.1106291\u003c/li\u003e\n\u003cli\u003eYokoyama C, Takayabu YN (2008) A Statistical Study on Rain Characteristics of Tropical Cyclones Using TRMM Satellite Data. Mon Weather Rev. 136: 3848\u0026ndash;3862. https://doi.org/10.1175/2008MWR2408.1\u003c/li\u003e\n\u003cli\u003eZhang S, Zhao H, Klotzbach PJ, Jiang X, Chen G, Chen S (2023) Interannual Variability in the Boreal Summer Intraseasonal Oscillation Modulates the Meridional Migration of Western North Pacific Tropical Cyclone Genesis. J Clim. 36(13): 4543\u0026ndash;4558. https://doi.org/10.1175/JCLI-D-22-0406.1\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Enhanced southwest monsoon, high precipitation events, rainfall variability, tropical cyclones","lastPublishedDoi":"10.21203/rs.3.rs-7273115/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7273115/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examines the role of the moisture conveyor belt (MCB) in high precipitation events (HPE) during the southwest monsoon (SWM) season in the Philippines. Using rainfall data from 11 weather stations along the country\u0026rsquo;s western coast, all high precipitation days (HPDs) from July to September between 1961 and 2022 were identified and categorized into direct tropical cyclone (TC), indirect TC, and SWM HPDs. A relationship between TC distance and intensity with HPD rainfall amount was not found, implying that there are other factors that modulate rainfall. HPEs were identified by grouping together HPDs that were less than 5 days apart. Lag composites of water vapor and surface latent heat fluxes revealed the presence of an MCB over the tropical northern Indian Ocean and the western North Pacific (WNP) during HPEs, but not during non-HPEs. This means that not all TCs to the northeast of the Philippines are able to produce HPEs. The presence of an MCB, which transports moisture from the Indian Ocean towards the Philippines, is integral to the occurrence of HPEs. Also, the Boreal Summer Intraseasonal Oscillation (BSISO) influences MCB formation and HPD occurrence. HPDs usually occur during Phases 5\u0026ndash;7 of the BSISO since enhanced convection over the WNP leads to the formation of TCs, while anomalous westerlies over the tropical northern Indian Ocean create the strong monsoon westerlies necessary for MCB formation. Understanding the role of the MCB in the occurrence of HPEs may help improve the forecasting of such events in the Philippines.\u003c/p\u003e","manuscriptTitle":"The Role of the Moisture Conveyor Belt in High Precipitation Events Along the Western Coast of the Philippines During the Southwest Monsoon Season","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-01 07:00:18","doi":"10.21203/rs.3.rs-7273115/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9850e7a7-5eb4-4576-8492-dae01b8442f0","owner":[],"postedDate":"September 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-22T07:08:14+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-01 07:00:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7273115","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7273115","identity":"rs-7273115","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}