Why does June rainfall over India have different variability and contribution to the seasonal rainfall compared to other months during the recent period?

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Prasanth Appukuttan Pillai, Suneeth KV, Kiran VG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4924420/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Climatic Change → Version 1 posted 4 You are reading this latest preprint version Abstract The present study observed a decreasing trend in rainfall over the Indian landmass during June month, while rainfall from July to September has an increasing trend in the recent decades. The contribution of June (September) rainfall to the seasonal total has also significantly decreased (increased) during the period. The study advances that the decreasing June rainfall trend results from reduced circulation, moisture advection, and convergence over India during June. The reduced convection trend over India has co-occurred with increased rainfall over the northwest Pacific and the equatorial Indian Ocean. Many of the seasonal strong (or weak) monsoon years during the study period exhibit opposite anomalies in June. Over the recent decades, the onset of the El Niño-Southern Oscillation (ENSO) has been delayed, shifting the ENSO-Monsoon teleconnection towards July-September. Additionally, the influence of North Tropical Atlantic (NTA) sea surface temperature (SST) anomalies on ISMR is also stronger towards the end of the monsoon season. Pre-monsoon SST anomalies in the Indian Ocean (IO) have shown a warming trend, leading to increased moisture advection and convection in the equatorial and northern Indian Ocean. These processes initiate early rainfall activity over the Indian land region towards the latter parts of May, creating an onset situation following a dry period during June, during which the influence of large-scale forcing is minimal. This leads to a decreased trend and lower rainfall in June compared to the rest of the season. Thus, June rainfall variability primarily depends on the pre-monsoon warming in the equatorial IO and the associated early propagation of monsoon onset, while rainfall in the other months is influenced by spring season NTA SST and concurrent ENSO anomalies during recent years. Indian summer monsoon rainfall monthly trend ENSO North tropical Atlantic SST Indian Ocean warming Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Indian summer monsoon rainfall (ISMR) during the four months from June to September accounts for more than 70% of the annual rainfall in the Indian land region. This rainfall is the primary source of livelihood for a large population that mainly depends on rainfed agriculture for food and economic stability. Although the seasonal rainfall variability is only about 10% of the seasonal mean, there is significant variability within the season (Goswami et al. 2003; Kulkarni et al. 2011). This variability is crucial for many activities related to agricultural planning (such as sowing, irrigation, and harvesting), transportation, and tourism, all of which influence the economic progress of the country. Thus, the prediction of ISMR in the sub-seasonal scale is important for the socio-economic benefits of the country. However, the prediction depends on the proper understanding of the processes responsible for this sub-seasonal variability of rainfall, mainly in the present decades of rapid climate variability. The present study aims to understand the variability of the Indian summer monsoon within the season on a monthly scale over recent decades and identify the possible contributors to this variability. For the seasonal mean ISMR and its interannual variability, the El Niño/Southern Oscillation (ENSO, e.g., Webster and Yang 1992; Ju and Slingo 1995; Soman and Slingo 1997; Lau and Nath 2000; Annamalai and Liu 2005; Pillai and Annamalai 2012 and many others) and the Indian Ocean Dipole (IOD, Webster et al. 1999; Saji et al. 1999; Ashok et al. 2003) are the major contributing factors. In the recent period, the role of North Atlantic SST in the ISMR is also discussed (Bohra et al., 2021; Pillai et al., 2023). Gill et al. (2015) examined the correlation between ENSO and ISMR for the entire season (JJAS), as well as for the early (June), middle (July–Aug), and late (September) sub-seasons, highlighting the inherent asymmetry in the ENSO-ISMR teleconnection. Ihara et al. (2008) discussed the monsoon variability over India on a monthly scale and its association with IOD during ENSO years with severe drought conditions (years where July rainfall is less than 16% of July's long-term mean). They showed that the evolution of the IOD in May/June during an El Niño year delays rainfall in July. A study by Kothwale and Kulkarni (2013) showed that June rainfall provides the first insight into the seasonal rainfall of the country. They further stated that this is due to the persistence of circulation anomalies formed during June throughout the season. Since the onset phase (June), the progressive phase (July–August), and the withdrawal phase (September) show different levels of variability, a monthly analysis of ISMR would be more effective in identifying the underlying mechanisms contributing to this variability. A study by Das et al. (2022) showed that the relationship between monthly rainfall over India and ENSO differs for individual months. During August, ISMR is mainly influenced by low-pressure systems in the Indian Ocean (IO). Borah et al. (2020) showed that in non-ENSO years, North Atlantic SST influences ISMR, and that influence occurs only towards the end of the season. They further concluded that these non-ENSO droughts were sub-seasonal and characterized by a steep decline in late-season rainfall. Our present analysis also noted that June's rainfall differs from other months during strong/weak years, and the association with boundary forcing is also different (Figures 1 and 2). Meanwhile, Chakravorty et al. (2016) showed that IO warming is one of the reasons for the decrease in rainfall in India during the onset period, and it can also cause an increase in rainfall during the withdrawal time. An earlier study by Roxy et al (2015) has shown that the JJAS seasonal monsoon rainfall over India has a decreasing trend induced by the decrease in land-sea thermal contrast over India during the period of intensified Indian Ocean warming. Studies on decadal variability have projected different ISMR trends based on the periods. The ISMR trend was stable from 1871 to 2010, but rainfall decreased in the last three decades of the twentieth century (Kulkarni 2012). The study concluded that after the mid-1970s, deficit monsoons became more frequent. However, recent studies have reported considerable spatial and temporal variability in ISMR trends (Goswami et al. 2006; Ghosh et al. 2012; 2016; Roxy et al. 2015; Devika and Pillai 2020). Studies that considered the season (June to September) as a whole and sub-seasonal wise have reached different conclusions regarding trends in ISMR mean and extreme rainfall (Guhathakurta and Rajeevan, 2008; Kumar et al., 2010; Ghosh et al., 2016). Trend analysis for the period 1901–2003 observed that in a few subdivisions, the contribution of summer rainfall in June, July, and September to the total annual rainfall is decreasing, while an increase is noted in August (Guhathakurta and Rajeevan 2008). Viswambharan et al. (2019) observed widespread spatial variability in the monthly trends of ISMR and concluded that seasonal trend analysis does not reveal the actual patterns in monthly ISMR trends. Our analysis (Fig. 1) noted a decreasing trend in June rainfall, while rainfall in the other three months (July, August, and September) is increasing in most of the sub-regions of the country. The study further shows that, in recent periods (the last 2-3 decades), June rainfall tends to be lower (higher) during many of the strong (weak) monsoon years (See Fig. 4b and 5b ). It is also evident that the contribution of June rainfall to the seasonal total has decreased during these periods, while the contribution of September rainfall has increased. For example, in recent years, such as 2010, 2012, 2016, 2019, 2021, 2022, and 2023, June rainfall was lower than September rainfall, and all these ended up with above-normal seasonal monsoon rainfall (see Fig. S1 in the supplementary section). A recent study by Maharana et al. (2019 ) showed that during June, the dust aerosol loading intensifies the monsoon shift to the Himalayan region, reducing that over central India through idealised modelling experiments. This shift in the monthly rainfall distribution and its contribution to the seasonal total may pose challenges for ISMR forecasting, potentially leading to overestimation/underestimation of mean rainfall (e.g., Das et al. 2022). Our study examines this issue and aims to understand the possible factors contributing to differences in monthly trends, different rainfall variability within the season, and their impact on seasonal rainfall. We analyze the trend of June rainfall compared to other summer months and investigate variables influencing these rainfall trends. Throughout the paper, the monthly trend is calculated for the entire period of 2001-2023(ie, trend for 23 years). We also investigate years with distinct rainfall anomalies for June and other months in the context of seasonal mean rainfall, whether above or below the normal. This analysis will help identify the factors to consider when forecasting seasonal and monthly rainfall for the Indian land region at a considerable lead time. 2. Data and Methodology The present study uses long-term daily and monthly gridded high-resolution (0.25 0 x0.25 0 ) rainfall data from the India Meteorological Department (IMD, Pai et al. 2014) to represent rainfall over India. Monthly rainfall data from the Global Precipitation Climatology Project (GPCP, Adler et al., 2003), having a horizontal resolution of 2.5 0 x2.5 0 for the latest period from 1979-2023, is also used. Sea-surface temperature data during the same period is taken from the U.K. Met Office Hadley Centre Sea ice and sea surface temperature data (HadISST, Rayner et al., 2003), which is at a regular grid of 1 0 x1 0 horizontal resolution. Atmospheric parameters such as zonal wind, meridional wind, specific humidity from 1000 to 300 hPa, and air temperature at a higher resolution of 0.25° x 0.25° are taken from the ERA-5 reanalysis products (Hersbach et al., 2020). The monthly trend is mainly calculated for the 1981-2023 period. However, to understand the recent change in the trend, the same analysis for the 1981-2000 and 2001-2023 periods are also performed. NOAA interpolated daily outgoing longwave radiation (OLR, Liebmann and Smith 1996) at 2.5 0 x2.5 0 horizontal resolution, which represents the daily convection, is also used for the same period. Moisture budget analysis will detail the relationship between moisture parameters and precipitation. The following equation can explain this, and more details can be found in Pillai and Annamalai (2012) (1) Predominantly, the balance between moisture advection and moisture convergence illustrates the role of moisture in convection. Moisture advection and convergence are calculated for different months, and the corresponding trends are also calculated. To understand changes in the mean rainfall and monthly contribution towards the seasonal mean, strong and weak years are selected based on standardized anomalies greater than +/-0.5 standard deviations (SD). Then, a composite is made for June when rainfall is stronger (weaker) than in other months, but monsoons are weaker (stronger). The list of these years is provided in Table 1. Composite analysis of June excess and deficit rainfall years is also carried out for rainfall, SST, 200 hPa velocity potential, etc., to understand the monthly changes in large-scale features associated with differences in the contribution of monthly rainfall to the seasonal mean. To understand changes during the early phase of the monsoon, onset dates are calculated using daily zonal wind at 850 hPa over the Arabia Sea region (5 o -15 o N, 40 o -80 o E) as defined by Wang et al. (2009). Daily OLR values are used to understand the propagation of daily convection during the selected years. Daily rainfall data from IMD is also used to observe the daily evolution of rainfall over the Indian land points for the years considered in the study. 3. Result and Discussion 3.1 Monthly trend of summer rainfall over India Figure 1 shows the spatial distribution of the total trend of June to September month rainfall over India for the last 23 years (2001-2023). During the monsoon onset month of June, rainfall exhibits a significant decreasing trend over the southwestern, western, and northeastern regions of Indian (Fig 1a). Conversely, there is an increasing trend in the southeast Indian region during this time. In contrast, the July-September months show an increasing trend in the southern, western, central, and northwestern parts of India, reducing the spatial distribution of negative trends to a minimum (Fig 1b-d). The spatial trend patterns obtained here have a resemblance to those obtained by Visambaran (2019) for 1980-2015 period. Figure S2 (in supplementary section) shows the time series (bold lines) and trend (dashed line) of all India land point averaged monthly rainfall for June, July, August and September months. This also indicates a decreasing trend for June month rainfall and an increasing trend for all the other three months with a slope of -1.43 for June and 2.54, 0.66, and 1.49 for the other three months from July to September, respectively. Extending the analysis back to 1981 reveals a similar trend pattern with a weaker magnitude, indicating a consistent trend pattern for monthly rainfall. A comparison of monthly rainfall for 23 years before and after 2000 indicates that after 2000, the seasonal rainfall of 852mm is contributed by 18.9% from June rainfall, 31.2% from July, 29% from August and 20.9 % from September rainfall. However, before 2000, the contributions were 19.7%, 32.5%, 28.8% and 19% respectively towards the seasonal mean of 860 mm. Thus, it highlights a difference in June rainfall tendencies compared to the rest of the monsoon season in recent periods. This may lead to a difference in monthly contribution to the seasonal mean rainfall during the study period. The present study investigates how these changes are reflected in ISMR and explores the causes or possible implications of this difference in the trend. It is established that rainfall is related to circulation anomalies, transport, and convergence of moisture. The June month trends in atmospheric circulation, moisture variability, and SST anomalies that can lead to rainfall variability over the Indian land mass for the same 23 years as in Fig 1, are provided in Figure 2. Associated with the decreasing June rainfall trend over India, there is an increasing rainfall trend over the northwestern Arabian Sea and south equatorial IO (Fig. 2a). The 850 hPa circulation pattern shows easterly wind anomalies over India converging in the northwestern Arabian Sea (AS), which favours anomalous moisture advection to the northern Indian Ocean from the Indian land region (Fig. 2c). This anomalous moisture advection shows an increasing trend in the Arabian sea (AS), close to the western boundary of India. The decrease in moisture advection is reflected in the moisture flux divergence trend over the Indian land mass, with a maximum in northeastern India. In contrast, the convergence trend is observed in the AS region, close to the west coast of India. The SST exhibits a warming trend across the Indo-west Pacific warm pool and tropical Atlantic regions. A weak moisture divergence pattern is observed over the Indian land region during this period (Fig. 2d). However, during July, the trend pattern of the above parameters began reversing in major parts of the monsoon region (Fig. 3). The large-scale tropical rainfall pattern shows an increasing trend in the equatorial western IO, the NWP, and the Indian land region (Fig. 3a). This is supported by the strong westerly cross-equatorial flow trend in the IO and the monsoon region. The Indian Ocean SST warming trend continues with a decreased strength. There is moisture advection towards the northern Indian land region, leading to a moisture flux convergence trend over the western coast of India and Indo-Gangetic plains (Fig. 3d). Thus, the observed negative trend of June month rainfall starts reversing by July even though the reversal of trend is not covering the entire Indian landmass during July. Similar analyses for August and September are performed and provided in Supplementary Figures S3 and S4. In August, there is an increased rainfall trend over the northern and western parts of India, along with the monsoon trough region, as seen in Fig. 1. The large-scale rainfall trend is also increasing in the Bay of Bengal (BoB) region. There is easterly wind convergence over the monsoon trough region, leading to an increased trend. The warm trend in the Indo-West Pacific persists during August as well. There is a moisture advection trend present in the southern region of India, and the MFC increases in the western side of India and the monsoon trough region, as shown by the increasing rainfall trend in Fig. 1c. During September month, the rainfall trend pattern is like that of July month, with increased westerly flow to the Indian land region. The warm SST trend persists throughout the monsoon season. Although there is a weak moisture advection trend over the Indian land region, the convergence trend is stronger during the study period. This indicates that the decreasing trend of June rainfall is associated with a corresponding shift in circulation pattern and moisture advection , leading to moisture convergence over the western AS during June. The convergence and advection towards the mainland region become prominent only from July onwards, with stronger advection to the Indo Gangetic Plains. The study addresses two important questions: How do these changes in trend or variability between the months of the boreal summer influence seasonal extreme rainfall, and what factors are responsible for the distinct changes observed in June compared to other months? These aspects are studied in detail. 3.2 Evaluation of monthly anomalies of ocean-atmosphere variables during June deficit and excess years In the next section, we analyze the monthly rainfall anomalies and the related large-scale parameters contributing to these anomalies during the study period. For this analysis, strong and weak monsoon years with JJAS seasonal rainfall anomalies exceeding ±0.5 SD are selected. The list of the years satisfying the criteria is provided in Table 1. We divided the years into those with June above average and other months below normal, resulting in weak JJAS and vice versa. The list of the years in both these categories is provided in Table 1. Here, the June deficit years are more in the study period, occurring mainly after 1990. Figure 4 shows the monthly anomalies of SST, velocity potential at 200 hPa, rainfall, and 850 hPa wind anomalies for the June deficit and the season above average years. In these cases, June exhibits weak cooling in the equatorial Pacific, with stronger warming in the tropical Atlantic region and moderate warming in the western IO region (Fig. 4a). The weak La Nina is unable to induce strong divergence anomalies over the central Pacific, leading to weak easterly wind anomalies. The convergent centre is in the North Tropical Atlantic (NTA, oceanic region bounded by 55 o W-15 o W, 5 o N-25 o N) extending to the African region, with easterly wind anomalies from the Indian land region (Fig. 4b). The wind anomalies over the equatorial IO are weak, and convection over the Indian land region is reduced. The La Nina SST anomalies and associated divergence over the equatorial east Pacific increase by July, inducing strong easterly wind anomalies extending to the monsoon region with strong convergent anomalies in the tropical eastern IO region. This is reflected in increased rainfall anomalies during the July-September months (Fig. 4c-h). Figure 5 is the same as Fig.4, but for June excess years, where the seasonal average ISMR is below normal. The large-scale patterns are almost the opposite of those in the June deficit cases. During June, the NTA experienced strong cooling, but the SST anomalies were very weak in the Indo-Pacific region. Associated with the divergence over the Atlantic are easterly wind anomalies to the northeastern Pacific and westerly flow from the African region to the Indian land region. There is increased flow from the northwest Pacific and IO to the monsoon region, resulting in increased rainfall there. However, by July, even though the NTA cooling persists, the El Niño-related warming and convergence become stronger in the equatorial east Pacific, resulting in stronger convergence. There is strong divergence and westerly wind anomalies over the Indian land region, leading to reduced rainfall throughout the remaining season. This results in below-average rainfall for the seasonal component. The analysis shows that the ENSO activity in the equatorial Pacific is weaker during June, which has opposite rainfall anomalies than the rest of the season and the seasonal average. However, the NTA anomalies and associated circulation remain stronger. The monthly analysis in the previous section indicates that the contribution of June rainfall to the seasonal mean is generally less but higher than September's contribution before 1980. However, the June contribution has decreased recently, while the September contribution has increased. 3.3 Role of North Atlantic SST anomalies in the monthly rainfall pattern The figures above show that the ocean-atmospheric variables exhibit opposite behaviour in June compared to the rest of the season. The developing ENSO was also weak during June, suggesting the possibility of other mechanisms that contributed during that period. Meanwhile, strong SST anomalies are present in the North Atlantic. A previous study by Borah et al. (2020) suggested that during non-ENSO strong/weak monsoon years, the North Atlantic SST plays a significant role in regulating the rainfall anomalies over the Indian land region. They also showed that during such years, rainfall is significantly modified at the end of the monsoon season compared to the early monsoon period. An earlier study by Chakravorthy et al. (2014) also noted a similar decrease during early phase and an increase during the August-September months. However, Pillai et al. (2023) have shown that the North Tropical Atlantic (NTA) SST modifies the co-occurring ENSO. These studies, along with Zhang and Huang (2022), indicate that instead of the pre-existing ENSO during boreal spring, ENSO is in a reversal phase after 2000 in MAM and starts strengthening by summer. However, these studies considered the monsoon season together. Here, at least in our composites in Fig. 4 and 5, when considering monthly evolution, the ENSO anomalies are weak during June, indicating that ENSO is stronger during the July-September period of the monsoon only. The role of NTA SST anomalies in this monthly evolution is worth studying. The SST anomalies in Figures 4a and 5a show the presence of strong warming (cooling) of North Atlantic SST anomalies, which are prominent during June and reduced as the season progresses. The ENSO anomalies form after the NTA SST anomalies, as Pillai et al. (2023) suggested that the NTA SST anomaly influences the formation of ENSO during the recent period. In the figures, the ENSO develops towards the middle and end of the monsoon season, and NTA's influence is supposed to be towards the end of the monsoon, as per Bhorah et al. (2020). A lead-lag correlation of monthly ISMR with Niño3.4 and NTA SST anomalies from previous (year -1) to simultaneous years (year 0) is conducted to understand the influence of these two SST indices more accurately and is depicted in Figure 6. The correlation indicates changes in the ENSO and NTA SST influence on monthly rainfall over India. For the season, ISMR is supposed to have a positive correlation with the previous year's winter Niño3.4, which decreases and changes to a significant negative correlation for the simultaneous Niño3.4 index (not shown). On the monthly scale, June has a weak negative correlation with ENSO from the previous year's summer onwards, attaining a maximum value of -0.28 by the second-year spring before decreasing. Thus, the relationship between June rainfall and ENSO is insignificant throughout the period. All the other months, except August, have a significant positive relationship with ENSO from the previous year's summer to winter, which reverses and strengthens during the simultaneous summer. The reduced correlation between August month rainfall and Nin3.4 is already discussed in Das et al. (2022). Similarly, NTA SST anomalies of the previous year had no relationship with monthly rainfall (Fig 6b). However, rainfall in August and September had a stronger relationship with NTA SST anomalies from spring to summer. Even though there is a solid simultaneous SST anomaly present in the NTA region during June, its simultaneous influence on June rainfall is weak and non-significant for the study period. Thus, the correlation also follows composite analysis performed in the above section, indicating that large-scale forcings such as ENSO and NTA SST anomalies influence the rainfall from July to September rather than June to September.It was also found that rainfall anomalies are stronger over the Arabian Sea during June. Similarly, the SST trend is also increasing in the IO. At this point, the present study estimates the role of pre-monsoon conditions of the IO that may contribute to June rainfall during the current period. 3.4 Role of May month conditions of the tropical Indian Ocean The previous analysis indicates a significant shift in the rainfall pattern in June, including its trend and contribution to seasonal mean anomalies, during the present period. Earlier studies, such as Chakraborty et al. (2016), have shown that the recent warming in the IO reduces Indian summer monsoon rainfall during the onset period (i.e., June and July mean) and increases the same during the withdrawal period (August and September mean). Similarly, Goswami et al. (2021) showed that the increased pre-monsoon rainfall in the Bay of Bengal is associated with decreased rainfall over India during the monsoon period. At the same time, Jiang and Li (2011) reported that rapid sea surface warming in the central BoB leads to a northward shift of the warmest SST axis from the equatorial Eastern Indian Ocean (EIO). This warming in the central BoB may precondition convective systems by destabilizing the local atmosphere, and the northward-propagating ISOs could initiate the monsoon onset by transferring moisture and momentum from the deep tropics (Zhou and Murtugudde 2014). Thus, this can lead to a sudden monsoon onset or “bogus onset,” during May itself. Previous studies have discussed this phenomenon, concluding that when a bogus onset occurs, the actual monsoon onset is delayed until the end of June, which may also reduce rainfall in June (Flatau et al., 2001). At the same time, this premature onset temporarily increases rainfall over the monsoon region for a few days at the end of May. This increase in ISMR is caused by the movement of the MJO convection branch from the IO to the Indian landmass. Kajikawa et al. (2012) further indicated that an early monsoon onset in the BoB during May can trigger an early advance of the monsoon in the South Asian region. This literature concludes that warming in the IO during the pre-monsoon period may contribute to increased rainfall in May, but it has an opposite effect on June rainfall. The following section analyzes the trends in May SST, moisture anomalies, and the role of May SST in influencing June rainfall. It also examines the daily rainfall and convection propagation associated with years of increased (or decreased) June rainfall. Figure 7 shows the May month trend in equatorial IO SST anomalies, moisture advection, MFC, precipitation, and 850 hPa wind anomalies during the study period. The figure shows a warming trend in the equatorial IO, particularly in the northern and central regions, consistent with earlier studies such as Chakraborty et al. (2016) and Goswami et al. (2021). This warming is associated with more substantial moisture advection and wind convergence trends, leading to increased moisture flux convergence over the northern IO region in May. Additionally, the MAM (March-April-May) SST trend in the Bay of Bengal is linked to enhanced local evaporation process and rainfall, resulting in increased convection north of the equatorial Indian Ocean, extending to the southern part of the Indian land region. These observations suggest that SST and local convective activity in the equatorial IO show an increasing trend during the pre-monsoon period, reflected in the increasing rainfall trend in the equatorial IO region during May.We have calculated composite differences of May month OLR and SST for the June dry and June wet cases to confirm that the change in trend is reflected in actual convection and is shown in Figure S5(supplementary section). It also shows that there is increased convection over equatorial IO region when June is dry. This is associated with similar increase in SST north and south of convection also. This indicates increased local convection in the equatorial IO associated with IO warming during May. Figure 8a and b compare rainfall and SST climatology differences between 2001-2020 and 1981-2000 for the month of May. The data show increased rainfall over the northern Indian Ocean (NIO), particularly in the AS and BoB after 2000. The NIO SST has also increased by more than 0.2°C. This confirms that, along with the trend, the actual rainfall and SST have also increased in the recent period, supporting earlier results. Figure 8 c-d shows the correlation between June rainfall over India and May SST anomalies for the study periods before (Fig. 8c) and after 2000 (Fig. 8d). In the recent period, a significant negative correlation is observed in the NIO, indicating that SST anomalies in this region are inversely related to June rainfall anomalies over India (Fig. 8d), a relationship that was not significant before 2000. This leads to the conclusion that the recent pre-monsoon warming of the equatorial Indian Ocean favours reduced rainfall over the Indian land region in June. However, the underlying mechanism for this behaviour requires further investigation. This can be achieved by considering the daily variability of rainfall and wind during the June deficit and excess years. Figure 9 shows the Hovmöller diagram of daily OLR propagation over Indian longitudes, representing the northward propagation of convection during both June deficit (Fig. 9a) and June excess (Fig. 9 b) cases. In years with weak June rainfall, convection starts moving northward from the equator by mid-May, weakens in early June, and intensifies over the Indian land region towards the end of June and early July. The convection becomes more vigorous and extends further over land regions in August and September. This indicates an early propagation of convection to the Indian land region at the end of May during the June deficit years, which is followed by reduced rainfall days during June. Conversely, in years with intense June rainfall, robust convection propagation towards the north occurs at the beginning of June. Then it propagates northward, covering the entire land region by month end. Figures 9a and b show differences in the propagation of convection, mainly in the initial stage, indicating that the onset of monsoon may be different during these two cases. This early movement of ISO supports the MJO advection towards the monsoon regions as proposed by the “bogus onset” study (Flatau et al., 2001). Figure 10 depicts the 850 hPa zonal wind (U850) over the Arabian Sea box (representing monsoon onset as per Wang et al., 2009) and the daily distribution of the rainfall anomalies over India for years with Excess and deficit June rainfall. For years with a June rainfall deficit, U850 attains the required onset strength of above 6.2 m/s by mid-May, then drops suddenly in early June before regaining strength later in June. This pattern is reflected in the total rainfall distribution over India, with slightly above-normal rainfall towards the end of May, a decrease until early July, followed by an increase in August and September. In contrast, during years with excess June rainfall, U850 attains the necessary strength by early June but remains weaker than its counterpart in July. The corresponding daily rainfall over India is also stronger during June and decreases in the following months. These two points match with previous analyses of the “bogus onset” phenomenon in ISMR studies, providing evidence of the influence of May month conditions on the June rainfall distribution. Thus, it indicates that the trends in tropical IO SST, rainfall, and circulation patterns influence the weakening of June rainfall over India. Also, in years with reduced June rainfall over India, there is an early accumulation of energy over IO for onset by mid-May. This leads to the propagation of rainfall belts into the monsoon region, which sustains until early June, followed by a break-like situation till the end of June. Conversely, in years with a normal onset of the monsoon in June, there is substantial rainfall during that month. The delayed effects of the large-scale boundary forcing from July to September will ultimately determine the rainfall for the remaining monsoon period. 4. Conclusion The present study analyzes the monthly rainfall distribution and its recent trends within the boreal summer season (June-September) period in India. A significant observation is that there is a decreasing trend of rainfall in June, with a reversal of this trend in the other months of the season during the recent 2-3 decades. Our study confirms that along with the decreasing trend of rainfall during June, there is a corresponding change in moisture advection, its convergence, and the lower-level winds over the monsoon region. The reduced rainfall trend over India during June corresponds with an increasing trend over the South China Sea and equatorial IO region. Interestingly, the trend of these parameters also reverses in July, aligning with the reversal in rainfall patterns. Our study highlights that June contribution to total seasonal rainfall has been historically low. However, after 2000, this contribution is again lower than the September rainfall contribution. Additionally, the study identified certain years where June experienced less (more) rainfall compared to other months, yet the overall season ended with a stronger (weaker) seasonal mean. Composites of these years indicate that weaker (stronger) June rainfall corresponds with warmer SST anomalies in the NTA region during the June-July months, while La Nina (El Niño) conditions begin developing from July onwards. This suggests that June lacks significant anomalies in large-scale tropical SST patterns, except in the NTA region. Notably, the relationship between NTA SST anomalies and ENSO during the monsoon season is evident. Still, the late development of ENSO has minimal impact on June rainfall anomalies, which often oppose the seasonal mean rainfall anomaly. Earlier studies, such as Borah et al. (2020), have demonstrated the influence of Atlantic SST on seasonal rainfall, particularly during the later parts of the monsoon period. These two factors contribute to seasonal rainfall but have a limited impact on June rainfall. Meanwhile, during the recent period of global warming, the equatorial and northern Indian Ocean has shown a stronger warming trend during the pre-monsoon period. This warming is associated with moisture convergence and rainfall anomalies in the equatorial IO during the pre-monsoon. These will enhance the propagation of convective bands towards the Indian land region, leading to early monsoon onset. Daily rainfall propagation and lower-level wind anomalies also indicate that in these years, there is an early monsoon onset, characterized by increased lower-level winds over the IO by mid-May and enhanced propagation in May. However, during June, these effects remain close to the equator and are associated with decreased velocity. However, in the reverse case, there is strong propagation to the monsoon region, but onset anomalies are stronger from June onwards. In summary, the study points out that in recent periods, increased warming over the IO induces convergence and increased convection over the equatorial north IO during the pre-monsoon period. This has often resulted in the early onset of the monsoon in recent years. This leads to increased rainfall in mid-May and decreased convection in June. For the rest of the season, from July to September, large-scale features such as ENSO and NTA SST play a role in setting up rainfall patterns, and these trends are becoming more pronounced in the 21 st century. Declarations Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Competing Interests The authors have no relevant financial or non-financial interests to disclose. Data Availability All the data sets used in the study are freely downloadable from different websites. The observed SST is taken from the HadISST (https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html, Rayner et al., 2004), and the wind parameters are taken from the ERA5 reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-complete?tab=overview). Hersbach et al., 2020). IMD gridded rainfall data are obtained from the following websites, IMD data: https://www.imdpune.gov.in/cmpg/Griddata/Rainfall_25_NetCDF.html. Global Precipitation Climatology Project (GPCP) Monthly Analysis Product data and NOAA Interpolated Outgoing Longwave Radiation (OLR) data are provided by the NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov. Processed data used for the study can be obtained from the first author on request for research. 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Supplementary Files supplimentaryfilesnew.pdf Cite Share Download PDF Status: Published Journal Publication published 09 Dec, 2025 Read the published version in Climatic Change → Version 1 posted Reviewers agreed at journal 16 Apr, 2025 Reviewers invited by journal 15 Apr, 2025 Editor assigned by journal 10 Apr, 2025 First submitted to journal 09 Apr, 2025 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-4924420","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":443427154,"identity":"65b0e823-6f28-4e74-812e-8e51b0a70ce8","order_by":0,"name":"Prasanth Appukuttan Pillai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYBCDBH5UvgFBHQYJkm0QlgTxWgyOoWjBA8ylDz/+8HPHnzzj+70PHxfm2NQxSB8+/IGh4A5OLZZ9aWaSvWcMis2OsRsbz9yWJsHAlwYkDJ7hdtEZBjMG3jaDxG3H2NikebcdlmDg4TEDih/Go4X988e/QC2b28Ba/gO18H/+gF8Lj4E0yJYNbGAtB0C2AMMAjxbLHp4yadk248QZx9KYgX5JlmzjYTOTSMCjxZyHffPHt21yif3NxxgfF26z4+fnYX784cMfPA5D5jCDCDYQkYBTAzYto2AUjIJRMArQAQC4SUlob9R7pQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-8159-6066","institution":"IITM: Indian Institute of Tropical Meteorology","correspondingAuthor":true,"prefix":"","firstName":"Prasanth","middleName":"Appukuttan","lastName":"Pillai","suffix":""},{"id":443427155,"identity":"136a8bf8-1809-4158-9b24-9a355307599b","order_by":1,"name":"Suneeth KV","email":"","orcid":"","institution":"IITM: Indian Institute of Tropical Meteorology","correspondingAuthor":false,"prefix":"","firstName":"Suneeth","middleName":"","lastName":"KV","suffix":""},{"id":443427156,"identity":"23932670-8715-44bc-be52-7518cd6ae0ac","order_by":2,"name":"Kiran VG","email":"","orcid":"","institution":"IITM: Indian Institute of Tropical Meteorology","correspondingAuthor":false,"prefix":"","firstName":"Kiran","middleName":"","lastName":"VG","suffix":""}],"badges":[],"createdAt":"2024-08-16 10:26:54","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4924420/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4924420/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10584-025-04089-x","type":"published","date":"2025-12-09T15:58:48+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80723986,"identity":"98dd254c-573b-4286-9cf9-8f1430bc3908","added_by":"auto","created_at":"2025-04-16 11:32:56","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":825870,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial pattern of monthly trend (in mm/dy for 23 23-year total) of IMD rainfall over India for the study period 2001- 2023 for (a) June, (b) July, (c) August, and (d) September months rainfall. The values above a 90% significance level are represented by dots in each panel\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/cd4407997d32aa68a3acd35e.png"},{"id":80725371,"identity":"46619cca-9c3a-40ba-973c-f738d463a80e","added_by":"auto","created_at":"2025-04-16 11:40:56","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1486635,"visible":true,"origin":"","legend":"\u003cp\u003eThe monthly total trend of 23 years for a) tropical rainfall (shaded) and 850 hPa wind (vector), (b) tropical SST, (c) moisture advection, and (d) moisture convergence for June for the twenty-three-year (2001-2023) period. Here in panel c, negative values denote an increased moisture advection trend, while in panel d, positive values indicate an increased moisture convergence trend. The values above 90% significance level are represented by dots in each panel\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/1da2396d7d6a7b5b8d2d7582.png"},{"id":80725373,"identity":"f5f0aad6-d603-48b0-9a80-d0f6e08867b3","added_by":"auto","created_at":"2025-04-16 11:40:56","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1246804,"visible":true,"origin":"","legend":"\u003cp\u003eThe monthly 23-year total trend of a) tropical rainfall (shaded) and 850 hPa wind (vector), (b) tropical SST, (c) moisture advection, and (d) moisture convergence for July for the twenty-three-year (2001-2023) period. Here in panel c, negative values denote an increased moisture advection trend, while in panel d, positive values indicate an increased moisture convergence trend. The values above a 90% significance level are represented by dots in each panel\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/5e67fe7c307d55a8da081dc1.jpeg"},{"id":80726253,"identity":"0f8d1458-64ae-4eb1-b601-c6b3e7a96564","added_by":"auto","created_at":"2025-04-16 11:48:56","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":301802,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly composite anomalies of a) SST (shaded) and velocity potential at 200 hPa (contour) b) precipitation (shaded) and 850 hPa wind (vector) over the tropical region for the composite of years with JJAS seasonal monsoon rainfall is above +0.5SD, but June month has deficit rainfall over India for 1981-2023 period. (c) and (d) are the same as (a) and (b), but for July, (e) and (f) for August, and (g) and (h) for September of the same composite years\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/091094ae82809068e5eb4ffa.png"},{"id":80726256,"identity":"71b74396-df17-4937-a363-84a1246d9d8e","added_by":"auto","created_at":"2025-04-16 11:48:56","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":312432,"visible":true,"origin":"","legend":"\u003cp\u003eMonthly composite anomalies of a) SST (shaded) and velocity potential at 200 hPa (contour) b) precipitation (shaded) and 850 hPa wind (vector) over the tropical region for the composite of years with JJAS seasonal monsoon rainfall is less than -0.5SD, but June month has excess rainfall over India during the 1981-2023 period. (c) and (d) are the same as (a) and (b), but for July, (e) and (f) for August, and (g) and (h) for September of the same composite years\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/546f78607bdd06cc0de7b45b.png"},{"id":80723993,"identity":"41527233-01c1-44a6-b974-372ed8a6acc6","added_by":"auto","created_at":"2025-04-16 11:32:56","extension":"jpeg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":538517,"visible":true,"origin":"","legend":"\u003cp\u003eLead-lag correlation of monthly rainfall averaged over the Indian land region with SST anomalies averaged over a) Niño3.4 region and b) for NTA SST anomalies from the previous year January (Jan (-1)) to the present year December (Dec (0))\u003c/p\u003e","description":"","filename":"floatimage6.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/ed4755bae54115387f500c81.jpeg"},{"id":80723994,"identity":"8a1fca63-da71-49c1-bd11-be861260871a","added_by":"auto","created_at":"2025-04-16 11:32:56","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1485644,"visible":true,"origin":"","legend":"\u003cp\u003eThe monthly total trend of 23 years for a) tropical rainfall (shaded) and 850 hPa wind (vector), (b) tropical SST, (c) moisture advection, and (d) moisture convergence for the May month for the twenty-three-year (2001-2023) period. Here in panel c, negative values denote an increased moisture advection trend, while in panel d, positive values indicate an increased moisture convergence trend. The values above 90% significance level are represented by dots in each panel\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/276aaa38fb0992fc096d157c.png"},{"id":80725376,"identity":"8c755557-4111-425d-b1c0-bc2e5f018fa2","added_by":"auto","created_at":"2025-04-16 11:40:56","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":110935,"visible":true,"origin":"","legend":"\u003cp\u003eDifference between May month climatology of two periods (ie, 2001 to 2020 and 1981-2000) for (a) rainfall, (b) SST. Subplots (c) and (d) are the correlation of June month rainfall averaged over the Indian land region with May month SST over the tropical Indian Ocean for the 1981-2000 and 2001-2020 period, respectively. Correlation values above 90% significance levels are only plotted in panels c and d\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/5323986bfd7636af74092e32.png"},{"id":80726259,"identity":"49f5a56c-9299-43d4-ad7f-b76820bbb6f1","added_by":"auto","created_at":"2025-04-16 11:48:56","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":211780,"visible":true,"origin":"","legend":"\u003cp\u003eHovmöller diagram showing propagation of OLR averaged over Indian longitudes, 65\u003csup\u003e0\u003c/sup\u003e- 100\u003csup\u003e0\u003c/sup\u003eE for composite of a) June excess years and (b) June excess years. Here, the daily OLR data is filtered using a 20-100 day band pass filter to retain the ISO variability before performing the analysis\u003c/p\u003e","description":"","filename":"floatimage9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/b3a067912e445127b6f65799.jpg"},{"id":80726257,"identity":"f43ac186-be34-45fd-9978-3c49813579d8","added_by":"auto","created_at":"2025-04-16 11:48:56","extension":"jpeg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":554599,"visible":true,"origin":"","legend":"\u003cp\u003ea) Time series of\u0026nbsp; zonal wind at 850 hPa (U850) averaged over the Arabian Sea region (5\u003csup\u003eo\u003c/sup\u003e-15\u003csup\u003eo\u003c/sup\u003eN, 40\u003csup\u003eo\u003c/sup\u003e-80\u003csup\u003eo\u003c/sup\u003eE) to represent the onset of the Indian summer monsoon (Wang et al 2009) from 1 May to 30 September for the composite of June excess (blue line) and June deficit (red line) years b)Time series of area-averaged daily rainfall over Indian land region from 1 May to 30 September for the same set of composite years\u003c/p\u003e","description":"","filename":"floatimage11.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/3f8019066065ba0988a7e0bc.jpeg"},{"id":98245090,"identity":"2d9b4459-8801-4fee-b56c-2adae766d55d","added_by":"auto","created_at":"2025-12-15 16:16:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6315272,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/f75603ae-fc7e-45e7-8e3c-12b6b5a2a6f8.pdf"},{"id":80725384,"identity":"39923da8-6d37-4748-b7b3-bda26b16cc40","added_by":"auto","created_at":"2025-04-16 11:40:56","extension":"pdf","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":649198,"visible":true,"origin":"","legend":"","description":"","filename":"supplimentaryfilesnew.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4924420/v1/5a6bf85506eb78253dabecc3.pdf"}],"financialInterests":"","formattedTitle":"Why does June rainfall over India have different variability and contribution to the seasonal rainfall compared to other months during the recent period?","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIndian summer monsoon rainfall (ISMR) during the four months from June to September accounts for more than 70% of the annual rainfall in the Indian land region. This rainfall is the primary source of livelihood for a large population that mainly depends on rainfed agriculture for food and economic stability. \u0026nbsp;Although the seasonal rainfall variability is only about 10% of the seasonal mean, there is significant variability within the season (Goswami et al. 2003; Kulkarni et al. 2011). This variability is crucial for many activities related to agricultural planning (such as sowing, irrigation, and harvesting), transportation, and tourism, all of which influence the economic progress of the country. Thus, the prediction of ISMR in the sub-seasonal scale is important for the socio-economic benefits of the country. However, the prediction depends on the proper understanding of the processes responsible for this sub-seasonal variability of rainfall, mainly in the present decades of rapid climate variability. \u0026nbsp;The present study aims to understand the variability of the Indian summer monsoon within the season on a monthly scale over recent decades and identify the possible contributors to this variability.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor the seasonal mean ISMR and its interannual variability, the El Ni\u0026ntilde;o/Southern Oscillation (ENSO, e.g., Webster and Yang 1992; Ju and Slingo 1995; Soman and Slingo 1997; Lau and Nath 2000; Annamalai and Liu 2005; Pillai and Annamalai 2012 and many others) and the Indian Ocean Dipole (IOD, Webster et al. 1999; Saji et al. 1999; Ashok et al. 2003) are the major contributing factors. In the recent period, the role of North Atlantic SST in the ISMR is also discussed (Bohra et al., 2021; Pillai et al., 2023). Gill et al. (2015) examined the correlation between ENSO and ISMR for the entire season (JJAS), as well as for the early (June), middle (July\u0026ndash;Aug), and late (September) sub-seasons, highlighting the inherent asymmetry in the ENSO-ISMR teleconnection. Ihara et al. (2008) discussed the monsoon variability over India on a monthly scale and its association with IOD during ENSO years with severe drought conditions (years where July rainfall is less than 16% of July\u0026apos;s long-term mean). They showed that the evolution of the IOD in May/June during an El Ni\u0026ntilde;o year delays rainfall in July.\u003c/p\u003e\n\u003cp\u003eA study by Kothwale and Kulkarni (2013) showed that June rainfall provides the first insight into the seasonal rainfall of the country. They further stated that this is due to the persistence of circulation anomalies formed during June throughout the season. Since the onset phase (June), the progressive phase (July\u0026ndash;August), and the withdrawal phase (September) show different levels of variability, a monthly analysis of ISMR would be more effective in identifying the underlying mechanisms contributing to this variability. A study by Das et al. (2022) showed that the relationship between monthly rainfall over India and ENSO differs for individual months. During August, ISMR is mainly influenced by low-pressure systems in the Indian Ocean (IO). Borah et al. (2020) showed that in non-ENSO years, North Atlantic SST influences ISMR, and that influence occurs only towards the end of the season. They further concluded that these non-ENSO droughts were sub-seasonal and characterized by a steep decline in late-season rainfall. Our present analysis also noted that June\u0026apos;s rainfall differs from other months during strong/weak years, and the association with boundary forcing is also different (Figures 1 and 2). Meanwhile, Chakravorty et al. (2016) showed that IO warming is one of the reasons for the decrease in rainfall in India during the onset period, and it can also cause an increase in rainfall during the withdrawal time. An earlier study by \u003cstrong\u003eRoxy et al (2015)\u003c/strong\u003e has shown that the JJAS seasonal monsoon rainfall over India has a decreasing trend induced by the decrease in land-sea thermal contrast over India during the period of intensified Indian Ocean warming.\u003c/p\u003e\n\u003cp\u003eStudies on decadal variability have projected different ISMR trends based on the periods. The ISMR trend was stable from 1871 to 2010, but rainfall decreased in the last three decades of the twentieth century (Kulkarni 2012). The study concluded that after the mid-1970s, deficit monsoons became more frequent. However, recent studies have reported considerable spatial and temporal variability in ISMR trends (Goswami et al. 2006; Ghosh et al. 2012; 2016; Roxy et al. 2015; Devika and Pillai 2020). Studies that considered the season (June to September) as a whole and sub-seasonal wise have reached different conclusions regarding trends in ISMR mean and extreme rainfall (Guhathakurta and Rajeevan, 2008; Kumar et al., 2010; Ghosh et al., 2016). Trend analysis for the period 1901\u0026ndash;2003 observed that in a few subdivisions, the contribution of summer rainfall in June, July, and September to the total annual rainfall is decreasing, while an increase is noted in August (Guhathakurta and Rajeevan 2008). Viswambharan et al. (2019) observed widespread spatial variability in the monthly trends of ISMR and concluded that seasonal trend analysis does not reveal the actual patterns in monthly ISMR trends.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Our analysis (Fig. 1) noted a decreasing trend in June rainfall, while rainfall in the other three months (July, August, and September) is increasing in most of the sub-regions of the country. The study further shows that, in recent periods (the last 2-3 decades), June rainfall tends to be lower (higher) during many of the strong (weak) monsoon years (See Fig. 4b and 5b ). It is also evident that the contribution of June rainfall to the seasonal total has decreased during these periods, while the contribution of September rainfall has increased. For example, in recent years, such as 2010, 2012, 2016, 2019, 2021, 2022, and 2023, June rainfall was lower than September rainfall, and all these ended up with above-normal seasonal monsoon rainfall (see Fig. S1 in the supplementary section). A recent study by \u003cstrong\u003eMaharana et al. (2019\u003c/strong\u003e) showed that during June, the dust aerosol loading intensifies the monsoon shift to the Himalayan region, reducing that over central India through idealised modelling experiments. This shift in the monthly rainfall distribution and its contribution to the seasonal total may pose challenges for ISMR forecasting, potentially leading to overestimation/underestimation of mean rainfall (e.g., Das et al. 2022). Our study examines this issue and aims to understand the possible factors contributing to differences in monthly trends, different rainfall variability within the season, and their impact on seasonal rainfall. We analyze the trend of June rainfall compared to other summer months and investigate variables influencing these rainfall trends. Throughout the paper, the monthly trend is calculated for the entire period of 2001-2023(ie, trend for 23 years). We also investigate years with distinct rainfall anomalies for June and other months in the context of seasonal mean rainfall, whether above or below the normal. This analysis will help identify the factors to consider when forecasting seasonal and monthly rainfall for the Indian land region at a considerable lead time.\u003c/p\u003e"},{"header":"2. Data and Methodology\t","content":"\u003cp\u003eThe present study uses long-term daily and monthly gridded high-resolution (0.25\u003csup\u003e0\u003c/sup\u003ex0.25\u003csup\u003e0\u003c/sup\u003e) rainfall data from the India Meteorological Department (IMD, Pai et al. 2014) to represent rainfall over India. Monthly rainfall data from the Global Precipitation Climatology Project (GPCP, Adler et al., 2003), having a horizontal resolution of 2.5\u003csup\u003e0\u003c/sup\u003ex2.5\u003csup\u003e0\u0026nbsp;\u003c/sup\u003efor the latest period from 1979-2023, is also used. Sea-surface temperature data during the same period is taken from the U.K. Met Office Hadley Centre Sea ice and sea surface temperature data (HadISST, Rayner et al., 2003), which is at a regular grid of 1\u003csup\u003e0\u003c/sup\u003ex1\u003csup\u003e0\u003c/sup\u003e horizontal resolution. \u0026nbsp;Atmospheric parameters such as zonal wind, meridional wind, specific humidity from 1000 to 300 hPa, and air temperature at a higher resolution of 0.25\u0026deg; x 0.25\u0026deg; are taken from the ERA-5 reanalysis products (Hersbach et al., 2020). The monthly trend is mainly calculated for the 1981-2023 period. However, to understand the recent change in the trend, the same analysis for the 1981-2000 and 2001-2023 periods are also performed. NOAA interpolated daily outgoing longwave radiation (OLR, Liebmann and Smith 1996) at 2.5\u003csup\u003e0\u003c/sup\u003ex2.5\u003csup\u003e0\u0026nbsp;\u003c/sup\u003ehorizontal resolution, which represents the daily convection, is also used for the same period.\u003c/p\u003e\n\u003cp\u003eMoisture budget analysis will detail the relationship between moisture parameters and precipitation. The following equation can explain this, and more details can be found in Pillai and Annamalai (2012)\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"219\" height=\"40\" src=\"https://myfiles.space/user_files/69519_bce2c0439cd956a6/69519_custom_files/img1744799726.png\" alt=\"image\"\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;(1)\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Predominantly, the balance between moisture advection and moisture convergence illustrates the role of moisture in convection. Moisture advection and convergence are calculated for different months, and the corresponding trends are also calculated.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo understand changes in the mean rainfall and monthly contribution towards the seasonal mean, strong and weak years are selected based on standardized anomalies greater than +/-0.5 standard deviations (SD). Then, a composite is made for June when rainfall is stronger (weaker) than in other months, but monsoons are weaker (stronger). The list of these years is provided in Table 1. Composite analysis of June excess and deficit rainfall years is also carried out for rainfall, SST, 200 hPa velocity potential, etc., to understand the monthly changes in large-scale features associated with differences in the contribution of monthly rainfall to the seasonal mean.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo understand changes during the early phase of the monsoon, onset dates are calculated using daily zonal wind at 850 hPa over the Arabia Sea region (5\u003csup\u003eo\u003c/sup\u003e-15\u003csup\u003eo\u003c/sup\u003eN, 40\u003csup\u003eo\u003c/sup\u003e-80\u003csup\u003eo\u003c/sup\u003eE) as defined by Wang et al. (2009). Daily OLR values are used to understand the propagation of daily convection during the selected years. Daily rainfall data from IMD is also used to observe the daily evolution of rainfall over the Indian land points for the years considered in the study.\u003c/p\u003e"},{"header":"3. Result and Discussion","content":"\u003cp\u003e\u003cstrong\u003e3.1 Monthly trend of summer rainfall over India\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure 1 shows the spatial distribution of the total trend of June to September month rainfall over India for the last 23 years (2001-2023). During the monsoon onset month of June, rainfall exhibits a significant decreasing trend over the southwestern, western, and northeastern regions of Indian (Fig 1a). Conversely, there is an increasing trend in the southeast Indian region during this time. In contrast, the July-September months show an increasing trend in the southern, western, central, and northwestern parts of India, reducing the spatial distribution of negative trends to a minimum (Fig 1b-d). The spatial trend patterns obtained here have a resemblance to those obtained by Visambaran (2019) for 1980-2015 period. Figure S2 (in supplementary section) shows the time series (bold lines) and trend (dashed line) of all India land point averaged monthly rainfall for June, July, August and September months. This also indicates a decreasing trend for June month rainfall and an increasing trend for all the other three months with a slope of -1.43 for June and 2.54, 0.66, and 1.49 for the other three months from July to September, respectively. Extending the analysis back to 1981 reveals a similar trend pattern with a weaker magnitude, indicating a consistent trend pattern for monthly rainfall. A comparison of monthly rainfall for 23 years before and after 2000 indicates that after 2000, the seasonal rainfall of 852mm is contributed by 18.9% from June rainfall, 31.2% from July, 29% from August and 20.9 % from September rainfall. However, before 2000, the contributions were 19.7%, 32.5%, 28.8% and 19% respectively towards the seasonal mean of 860 mm.\u0026nbsp;Thus, it highlights a difference in June rainfall tendencies compared to the rest of the monsoon season in recent periods. This may lead to a difference in monthly contribution to the seasonal mean rainfall during the study period. The present study investigates how these changes are reflected in ISMR and explores the causes or possible implications of this difference in the trend.\u003c/p\u003e\n\u003cp\u003eIt is established that rainfall is related to circulation anomalies, transport, and convergence of moisture. The June month trends in atmospheric circulation, moisture variability, and SST anomalies that can lead to rainfall variability over the Indian land mass for the same 23 years as in Fig 1, are provided in Figure 2. Associated with the decreasing June rainfall trend over India, there is an increasing rainfall trend over the northwestern Arabian Sea and south equatorial IO (Fig. 2a). The 850 hPa circulation pattern shows easterly wind anomalies over India converging in the northwestern Arabian Sea (AS), which favours anomalous moisture advection to the northern Indian Ocean from the Indian land region (Fig. 2c). This anomalous moisture advection shows an increasing trend in the Arabian sea (AS), close to the western boundary of India. The decrease in moisture advection is reflected in the moisture flux divergence trend over the Indian land mass, with a maximum in northeastern India. In contrast, the convergence trend is observed in the AS region, close to the west coast of India. \u0026nbsp;The SST exhibits a warming trend across the Indo-west Pacific warm pool and tropical Atlantic regions. A weak moisture divergence pattern is observed over the Indian land region during this period (Fig. 2d).\u003c/p\u003e\n\u003cp\u003eHowever, during July, the trend pattern of the above parameters began reversing in major parts of the monsoon region (Fig. 3). The large-scale tropical rainfall pattern shows an increasing trend in the equatorial western IO, the NWP, and the Indian land region (Fig. 3a). This is supported by the strong westerly cross-equatorial flow trend in the IO and the monsoon region. The Indian Ocean SST warming trend continues with a decreased strength. There is moisture advection towards the northern Indian land region, leading to a moisture flux convergence trend over the western coast of India and Indo-Gangetic plains (Fig. 3d). \u0026nbsp;Thus, the observed negative trend of June month rainfall starts reversing by July even though the reversal of trend is not covering the entire Indian landmass during July.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Similar analyses for August and September are performed and provided in Supplementary Figures S3 and S4. In August, there is an increased rainfall trend over the northern and western parts of India, along with the monsoon trough region, as seen in Fig. 1. The large-scale rainfall trend is also increasing in the Bay of Bengal (BoB) region. There is easterly wind convergence over the monsoon trough region, leading to an increased trend. The warm trend in the Indo-West Pacific persists during August as well. There is a moisture advection trend present in the southern region of India, and the MFC increases in the western side of India and the monsoon trough region, as shown by the increasing rainfall trend in Fig. 1c. During September month, the rainfall trend pattern is like that of July month, with increased westerly flow to the Indian land region. The warm SST trend persists throughout the monsoon season. Although there is a weak moisture advection trend over the Indian land region, the convergence trend is stronger during the study period.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis indicates that the decreasing trend of June rainfall is associated with a corresponding shift in circulation pattern and moisture advection\u003cem\u003e,\u003c/em\u003e leading to moisture convergence over the western AS during June. The convergence and advection towards the mainland region become prominent only from July onwards, with stronger advection to the Indo Gangetic Plains. The study addresses two important questions: How do these changes in trend or variability between the months of the boreal summer influence seasonal extreme rainfall, and what factors are responsible for the distinct changes observed in June compared to other months? These aspects are studied in detail.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Evaluation of monthly anomalies of ocean-atmosphere variables during June deficit and excess years\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the next section, we analyze the monthly rainfall anomalies and the related large-scale parameters contributing to these anomalies during the study period. For this analysis, strong and weak monsoon years with JJAS seasonal rainfall anomalies exceeding \u0026plusmn;0.5 SD are selected. The list of the years satisfying the criteria is provided in Table 1. We divided the years into those with June above average and other months below normal, resulting in weak JJAS and vice versa. The list of the years in both these categories is provided in Table 1. Here, the June deficit years are more in the study period, occurring mainly after 1990.\u003c/p\u003e\n\u003cp\u003eFigure 4 shows the monthly anomalies of SST, velocity potential at 200 hPa, rainfall, and 850 hPa wind anomalies for the June deficit and the season above average years. In these cases, June exhibits weak cooling in the equatorial Pacific, with stronger warming in the tropical Atlantic region and moderate warming in the western IO region (Fig. 4a). The weak La Nina is unable to induce strong divergence anomalies over the central Pacific, leading to weak easterly wind anomalies. The convergent centre is in the North Tropical Atlantic (NTA, oceanic region bounded by 55\u003csup\u003eo\u003c/sup\u003eW-15\u003csup\u003eo\u003c/sup\u003eW, 5\u003csup\u003eo\u003c/sup\u003eN-25\u003csup\u003eo\u003c/sup\u003eN) extending to the African region, with easterly wind anomalies from the Indian land region (Fig. 4b). The wind anomalies over the equatorial IO are weak, and convection over the Indian land region is reduced. The La Nina SST anomalies and associated divergence over the equatorial east Pacific increase by July, inducing strong easterly wind anomalies extending to the monsoon region with strong convergent anomalies in the tropical eastern IO region. This is reflected in increased rainfall anomalies during the July-September months (Fig. 4c-h).\u003c/p\u003e\n\u003cp\u003eFigure 5 is the same as Fig.4, but for June excess years, where the seasonal average ISMR is below normal. The large-scale patterns are almost the opposite of those in the June deficit cases. During June, the NTA experienced strong cooling, but the SST anomalies were very weak in the Indo-Pacific region. Associated with the divergence over the Atlantic are easterly wind anomalies to the northeastern Pacific and westerly flow from the African region to the Indian land region. There is increased flow from the northwest Pacific and IO to the monsoon region, resulting in increased rainfall there. However, by July, even though the NTA cooling persists, the El Ni\u0026ntilde;o-related warming and convergence become stronger in the equatorial east Pacific, resulting in stronger convergence. There is strong divergence and westerly wind anomalies over the Indian land region, leading to reduced rainfall throughout the remaining season. This results in below-average rainfall for the seasonal component.\u003c/p\u003e\n\u003cp\u003eThe analysis shows that the ENSO activity in the equatorial Pacific is weaker during June, which has opposite rainfall anomalies than the rest of the season and the seasonal average. However, the NTA anomalies and associated circulation remain stronger. The monthly analysis in the previous section indicates that the contribution of June rainfall to the seasonal mean is generally less but higher than September\u0026apos;s contribution before 1980. However, the June contribution has decreased recently, while the September contribution has increased.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3\u003c/strong\u003e \u003cstrong\u003eRole of North Atlantic SST anomalies in the monthly rainfall pattern\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe figures above show that the ocean-atmospheric variables exhibit opposite behaviour in June compared to the rest of the season. The developing ENSO was also weak during June, suggesting the possibility of other mechanisms that contributed during that period. Meanwhile, strong SST anomalies are present in the North Atlantic. A previous study by Borah et al. (2020) suggested that during non-ENSO strong/weak monsoon years, the North Atlantic SST plays a significant role in regulating the rainfall anomalies over the Indian land region. They also showed that during such years, rainfall is significantly modified at the end of the monsoon season compared to the early monsoon period. An earlier study by Chakravorthy et al. (2014) also noted a similar decrease during early phase and an increase during the August-September months. However, Pillai et al. (2023) have shown that the North Tropical Atlantic (NTA) SST modifies the co-occurring ENSO. These studies, along with Zhang and Huang (2022), indicate that instead of the pre-existing ENSO during boreal spring, ENSO is in a reversal phase after 2000 in MAM and starts strengthening by summer. However, these studies considered the monsoon season together. Here, at least in our composites in Fig. 4 and 5, when considering monthly evolution, the ENSO anomalies are weak during June, indicating that ENSO is stronger during the July-September period of the monsoon only.\u003c/p\u003e\n\u003cp\u003eThe role of NTA SST anomalies in this monthly evolution is worth studying. \u0026nbsp;The SST anomalies in Figures 4a and 5a show the presence of strong warming (cooling) of North Atlantic SST anomalies, which are prominent during June and reduced as the season progresses. The ENSO anomalies form after the NTA SST anomalies, as Pillai et al. (2023) suggested that the NTA SST anomaly influences the formation of ENSO during the recent period. In the figures, the ENSO develops towards the middle and end of the monsoon season, and NTA\u0026apos;s influence is supposed to be towards the end of the monsoon, as per Bhorah et al. (2020).\u003c/p\u003e\n\u003cp\u003eA lead-lag correlation of monthly ISMR with Ni\u0026ntilde;o3.4 and NTA SST anomalies from previous (year -1) to simultaneous years (year 0) is conducted to understand the influence of these two SST indices more accurately and is depicted in Figure 6. The correlation indicates changes in the ENSO and NTA SST influence on monthly rainfall over India. For the season, ISMR is supposed to have a positive correlation with the previous year\u0026apos;s winter Ni\u0026ntilde;o3.4, which decreases and changes to a significant negative correlation for the simultaneous Ni\u0026ntilde;o3.4 index (not shown). On the monthly scale, June has a weak negative correlation with ENSO from the previous year\u0026apos;s summer onwards, attaining a maximum value of -0.28 by the second-year spring before decreasing. Thus, the relationship between June rainfall and ENSO is insignificant throughout the period. All the other months, except August, have a significant positive relationship with ENSO from the previous year\u0026apos;s summer to winter, which reverses and strengthens during the simultaneous summer. The reduced correlation between August month rainfall and Nin3.4 is already discussed in Das et al. (2022). \u0026nbsp; Similarly, NTA SST anomalies of the previous year had no relationship with monthly rainfall (Fig 6b). However, rainfall in August and September had a stronger relationship with NTA SST anomalies from spring to summer. Even though there is a solid simultaneous SST anomaly present in the NTA region during June, its simultaneous influence on June rainfall is weak and non-significant for the study period. \u0026nbsp;Thus, the correlation also follows composite analysis performed in the above section, indicating that large-scale forcings such as ENSO and NTA SST anomalies influence the rainfall from July to September rather than June to September.It was also found that rainfall anomalies are stronger over the Arabian Sea during June. Similarly, the SST trend is also increasing in the IO. At this point, the present study estimates the role of pre-monsoon conditions of the IO that may contribute to June rainfall during the current period.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Role of May month conditions of the tropical Indian Ocean\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe previous analysis indicates a significant shift in the rainfall pattern in June, including its trend and contribution to seasonal mean anomalies, during the present period. Earlier studies, such as Chakraborty et al. (2016), have shown that the recent warming in the IO reduces Indian summer monsoon rainfall during the onset period (i.e., June and July mean) and increases the same during the withdrawal period (August and September mean). Similarly, Goswami et al. (2021) showed that the increased pre-monsoon rainfall in the Bay of Bengal is associated with decreased rainfall over India during the monsoon period. At the same time, Jiang and Li (2011) reported that rapid sea surface warming in the central BoB leads to a northward shift of the warmest SST axis from the equatorial Eastern Indian Ocean (EIO). This warming in the central BoB may precondition convective systems by destabilizing the local atmosphere, and the northward-propagating ISOs could initiate the monsoon onset by transferring moisture and momentum from the deep tropics (Zhou and Murtugudde 2014). Thus, this can lead to a sudden monsoon onset or \u0026ldquo;bogus onset,\u0026rdquo; during May itself. Previous studies have discussed this phenomenon, concluding that when a bogus onset occurs, the actual monsoon onset is delayed until the end of June, which may also reduce rainfall in June (Flatau et al., 2001). At the same time, this premature onset temporarily increases rainfall over the monsoon region for a few days at the end of May. This increase in ISMR is caused by the movement of the MJO convection branch from the IO to the Indian landmass. Kajikawa et al. (2012) further indicated that an early monsoon onset in the BoB during May can trigger an early advance of the monsoon in the South Asian region. This literature concludes that warming in the IO during the pre-monsoon period may contribute to increased rainfall in May, but it has an opposite effect on June rainfall. The following section analyzes the trends in May SST, moisture anomalies, and the role of May SST in influencing June rainfall. It also examines the daily rainfall and convection propagation associated with years of increased (or decreased) June rainfall.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Figure 7 shows the May month trend\u0026nbsp;in equatorial IO SST anomalies, moisture advection, MFC, precipitation, and 850 hPa wind anomalies during the study period. The figure shows a warming trend in the equatorial IO, particularly in the northern and central regions, consistent with earlier studies such as Chakraborty et al. (2016) and Goswami et al. (2021). This warming is associated with more substantial moisture advection and wind convergence trends, leading to increased moisture flux convergence over the northern IO region in May. Additionally, the MAM (March-April-May) SST trend in the Bay of Bengal is linked to enhanced local evaporation process and rainfall, resulting in increased convection north of the equatorial Indian Ocean, extending to the southern part of the Indian land region. These observations suggest that SST and local convective activity in the equatorial IO show an increasing trend during the pre-monsoon period, reflected in the increasing rainfall trend in the equatorial IO region during May.We have calculated composite differences of May month OLR and SST for the June dry and June wet cases to confirm that the change in trend is reflected in actual convection and is shown in Figure S5(supplementary section). It also shows that there is increased convection over equatorial IO region when June is dry. This is associated with similar increase in SST north and south of convection also.\u0026nbsp;\u0026nbsp;This indicates increased local convection in the equatorial IO associated with IO warming during May.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFigure 8a and b compare rainfall and SST climatology differences between 2001-2020 and 1981-2000 for the month of May. The data show increased rainfall over the northern Indian Ocean (NIO), particularly in the AS and BoB after 2000. The NIO SST has also increased by more than\u0026nbsp;0.2\u0026deg;C. This confirms that, along with the trend, the actual rainfall and SST have also increased in the recent period, supporting earlier results. Figure 8 c-d shows the correlation between June rainfall over India and May SST anomalies for the study periods before (Fig. 8c) and after 2000 (Fig. 8d). In the recent period, a significant negative correlation is observed in the NIO, indicating that SST anomalies in this region are inversely related to June rainfall anomalies over India (Fig. 8d), a relationship that was not significant before 2000. This leads to the conclusion that the recent pre-monsoon warming of the equatorial Indian Ocean favours reduced rainfall over the Indian land region in June. However, the underlying mechanism for this behaviour requires further investigation. This can be achieved by considering the daily variability of rainfall and wind during the June deficit and excess years.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Figure 9 shows the Hovm\u0026ouml;ller diagram of daily OLR propagation over Indian longitudes, representing the northward propagation of convection during both June deficit (Fig. 9a) and June excess (Fig. 9 b) cases. In years with weak June rainfall, convection starts moving northward from the equator by mid-May, weakens in early June, and intensifies over the Indian land region towards the end of June and early July. The convection becomes more vigorous and extends further over land regions in August and September. This indicates an early propagation of convection to the Indian land region at the end of May during the June deficit years, which is followed by reduced rainfall days during June. \u0026nbsp;Conversely, in years with intense June rainfall, robust convection propagation towards the north occurs at the beginning of June. Then it propagates northward, covering the entire land region by month end. Figures 9a and b show differences in the propagation of convection, mainly in the initial stage, indicating that the onset of monsoon may be different during these two cases. This early movement of ISO supports the MJO advection towards the monsoon regions as proposed by the \u0026ldquo;bogus onset\u0026rdquo; study (Flatau et al., 2001).\u003c/p\u003e\n\u003cp\u003eFigure 10 depicts the 850 hPa zonal wind (U850) over the Arabian Sea box (representing monsoon onset as per Wang et al., 2009) and the daily distribution of the rainfall anomalies over India for years with Excess and deficit June rainfall. For years with a June rainfall deficit, U850 attains the required onset strength of above 6.2 m/s by mid-May, then drops suddenly in early June before regaining strength later in June. This pattern is reflected in the total rainfall distribution over India, with slightly above-normal rainfall towards the end of May, a decrease until early July, followed by an increase in August and September. In contrast, during years with excess June rainfall, U850 attains the necessary strength by early June but remains weaker than its counterpart in July. The corresponding daily rainfall over India is also stronger during June and decreases in the following months.\u003c/p\u003e\n\u003cp\u003eThese two points match with previous analyses of the \u0026ldquo;bogus onset\u0026rdquo; phenomenon in ISMR studies, providing evidence of the influence of May month conditions on the June rainfall distribution. Thus, it indicates that the trends in tropical IO SST, rainfall, and circulation patterns influence the weakening of June rainfall over India. Also, in years with reduced June rainfall over India, there is an early accumulation of energy over IO for onset by mid-May. This leads to the propagation of rainfall belts into the monsoon region, which sustains until early June, followed by a break-like situation till the end of June. Conversely, in years with a normal onset of the monsoon in June, there is substantial rainfall during that month. The delayed effects of the large-scale boundary forcing from July to September will ultimately determine the rainfall for the remaining monsoon period.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe present study analyzes the monthly rainfall distribution and its recent trends within the boreal summer season (June-September) period in India. A significant observation is that there is a decreasing trend of rainfall in June, with a reversal of this trend in the other months of the season during the recent 2-3 decades. Our study confirms that along with the decreasing trend of rainfall during June, there is a corresponding change in moisture advection, its convergence, and the lower-level winds over the monsoon region. The reduced rainfall trend over India during June corresponds with an increasing trend over the South China Sea and equatorial IO region. \u0026nbsp;Interestingly, the trend of these parameters also reverses in July, aligning with the reversal in rainfall patterns.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study highlights that June contribution to total seasonal rainfall has been historically low. However, after 2000, this contribution is again lower than the September rainfall contribution. Additionally, the study identified certain years where June experienced less (more) rainfall compared to other months, yet the overall season ended with a stronger (weaker) seasonal mean. Composites of these years indicate that weaker (stronger) June rainfall corresponds with warmer SST anomalies in the NTA region during the June-July months, while La Nina (El Ni\u0026ntilde;o) conditions begin developing from July onwards. This suggests that June lacks significant anomalies in large-scale tropical SST patterns, except in the NTA region. Notably, the relationship between NTA SST anomalies and ENSO during the monsoon season is evident. Still, the late development of ENSO has minimal impact on June rainfall anomalies, which often oppose the seasonal mean rainfall anomaly. Earlier studies, such as Borah et al. (2020), have demonstrated the influence of Atlantic SST on seasonal rainfall, particularly during the later parts of the monsoon period. These two factors contribute to seasonal rainfall but have a limited impact on June rainfall.\u003c/p\u003e\n\u003cp\u003eMeanwhile, during the recent period of global warming, the equatorial and northern Indian Ocean has shown a stronger warming trend during the pre-monsoon period. This warming is associated with moisture convergence and rainfall anomalies in the equatorial IO during the pre-monsoon. These will enhance the propagation of convective bands towards the Indian land region, leading to early monsoon onset. Daily rainfall propagation and lower-level wind anomalies also indicate that in these years, there is an early monsoon onset, characterized by increased lower-level winds over the IO by mid-May and enhanced propagation in May. However, during June, these effects remain close to the equator and are associated with decreased velocity. However, in the reverse case, there is strong propagation to the monsoon region, but onset anomalies are stronger from June onwards.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn summary, the study points out that in recent periods, increased warming over the IO induces convergence and increased convection over the equatorial north IO during the pre-monsoon period. This has often resulted in the early onset of the monsoon in recent years. This leads to increased rainfall in mid-May and decreased convection in June. For the rest of the season, from July to September, large-scale features such as ENSO and NTA SST play a role in setting up rainfall patterns, and these trends are becoming more pronounced in the 21\u003csup\u003est\u003c/sup\u003e century.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no relevant financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data sets used in the study are freely downloadable from different websites. The observed SST is taken from the HadISST (https://www.metoffice.gov.uk/hadobs/hadisst/data/download.html, Rayner et al., 2004), and the wind parameters are taken from the ERA5 reanalysis (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-complete?tab=overview). Hersbach et al., 2020). IMD gridded rainfall data are obtained from the following websites, IMD data: https://www.imdpune.gov.in/cmpg/Griddata/Rainfall_25_NetCDF.html. Global Precipitation Climatology Project (GPCP) Monthly Analysis Product data and NOAA Interpolated Outgoing Longwave Radiation (OLR) data are provided by the NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov. Processed data used for the study can be obtained from the first author on request for research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAnnamalai\u0026nbsp;H, Liu P\u0026nbsp;(2005)\u0026nbsp;Response of the Asian summer monsoon to changes in El Ni\u0026ntilde;o properties.\u0026nbsp;\u003cem\u003eQuart. J. Roy. Meteor. Soc.\u003c/em\u003e,\u0026nbsp;\u003cstrong\u003e131\u003c/strong\u003e,\u0026nbsp;805\u0026ndash;831.\u003c/li\u003e\n \u003cli\u003eAshok K, Guan Z, Saji N, Yamagata T (2004) Individual and combined influences of ENSO and the Indian Ocean dipole on the Indian summer monsoon. J Clim 17:3141\u0026ndash;3155.\u003c/li\u003e\n \u003cli\u003eAdler RF, Huffman GJ, Chang A, Ferraro R, Xie P, Janowiak J, Rudolf B, Schneider U, Curtis S, Bolvin D, Gruber A, Susskind J, Arkin P (2003) The Version 2 Global Precipitation Climatology Project (GPCP) Monthly Precipitation Analysis (1979-Present). 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J Clim 27:126\u0026ndash;139.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Indian summer monsoon rainfall, monthly trend, ENSO, North tropical Atlantic SST, Indian Ocean warming","lastPublishedDoi":"10.21203/rs.3.rs-4924420/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4924420/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"The present study observed a decreasing trend in rainfall over the Indian landmass during June month, while rainfall from July to September has an increasing trend in the recent decades. The contribution of June (September) rainfall to the seasonal total has also significantly decreased (increased) during the period. The study advances that the decreasing June rainfall trend results from reduced circulation, moisture advection, and convergence over India during June. The reduced convection trend over India has co-occurred with increased rainfall over the northwest Pacific and the equatorial Indian Ocean. Many of the seasonal strong (or weak) monsoon years during the study period exhibit opposite anomalies in June. Over the recent decades, the onset of the El Niño-Southern Oscillation (ENSO) has been delayed, shifting the ENSO-Monsoon teleconnection towards July-September. Additionally, the influence of North Tropical Atlantic (NTA) sea surface temperature (SST) anomalies on ISMR is also stronger towards the end of the monsoon season. Pre-monsoon SST anomalies in the Indian Ocean (IO) have shown a warming trend, leading to increased moisture advection and convection in the equatorial and northern Indian Ocean. These processes initiate early rainfall activity over the Indian land region towards the latter parts of May, creating an onset situation following a dry period during June, during which the influence of large-scale forcing is minimal. This leads to a decreased trend and lower rainfall in June compared to the rest of the season. Thus, June rainfall variability primarily depends on the pre-monsoon warming in the equatorial IO and the associated early propagation of monsoon onset, while rainfall in the other months is influenced by spring season NTA SST and concurrent ENSO anomalies during recent years.","manuscriptTitle":"Why does June rainfall over India have different variability and contribution to the seasonal rainfall compared to other months during the recent period?","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-16 11:32:51","doi":"10.21203/rs.3.rs-4924420/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"","date":"2025-04-16T04:14:06+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-15T17:28:29+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-10T12:35:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"Climatic Change","date":"2025-04-09T08:17:39+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"climatic-change","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"clim","sideBox":"Learn more about [Climatic Change](https://www.springer.com/journal/10584)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/clim/default.aspx","title":"Climatic Change","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"1233f4e5-cd58-43f4-ad99-e9d9303e3fd3","owner":[],"postedDate":"April 16th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-12-15T16:11:45+00:00","versionOfRecord":{"articleIdentity":"rs-4924420","link":"https://doi.org/10.1007/s10584-025-04089-x","journal":{"identity":"climatic-change","isVorOnly":false,"title":"Climatic Change"},"publishedOn":"2025-12-09 15:58:48","publishedOnDateReadable":"December 9th, 2025"},"versionCreatedAt":"2025-04-16 11:32:51","video":"","vorDoi":"10.1007/s10584-025-04089-x","vorDoiUrl":"https://doi.org/10.1007/s10584-025-04089-x","workflowStages":[]},"version":"v1","identity":"rs-4924420","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4924420","identity":"rs-4924420","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}