Moisture sources and isotopic composition of Meiyu-Baiu rainfall in southwestern Japan from 2004 to 2023

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Asian Monsoon (AM) moisture, transported by the monsoonal southwesterlies in middle levels, contributed 50.9% of the total rainfall with low δ 2 H and high d-excess. Conversely, North Pacific subtropical high (NPSH) moisture in low levels accounted for 28.6% of the total rainfall with high δ 2 H and low d-excess. AM moisture during heavy rainfall seasons exhibited lower δ 2 H and higher d-excess, due to more rainout and below-cloud evaporation. In contrast, the isotopic signals of NPSH moisture were relatively consistent between heavy and light seasons. Extreme rainfall showed lower δ 2 H and higher d-excess with more contribution (57.8%) from AM moisture at middle levels with high precipitation efficiency, which facilitates deep convection, triggering more extreme rainfall events during heavy seasons. The study highlights importance of AM moisture for extreme Meiyu-Baiu rainfall in East Asia. The findings providing valuable insights into understanding the interannual variability of water cycle in East Asia, as well as to improving seasonal forecasts and near-future predictions of Meiyu-Baiu rainfall. Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric chemistry Earth and environmental sciences/Climate sciences/Hydrology Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Meiyu-Baiu rainfall is an important component of summer rainfall in East Asia and is characterized by a midlatitude front with a tropical convective nature 1 , 2 . The interannual variability of Meiyu-Baiu rainfall, in terms of its position and intensity, is closely linked to floods and droughts, leading to significant socioeconomic losses in East Asia 3 – 5 . Generally, the variability of Meiyu-Baiu rainfall is influenced by the Asian monsoon (AM) southwesterlies 6 – 8 , the North Pacific subtropical high (NPSH) 9 , 10 , and the East Asian mid-latitude westerlies 11 , 12 . During the Meiyu-Baiu period in early summer, large amounts of moisture are transported to East Asia by monsoonal southwesterlies from the Indian Ocean 13 – 15 and by the NPSH from the Pacific Ocean 16 – 18 . Previous studies have highlighted the importance of moisture from the Indian Ocean 7 , 8 , Pacific Ocean 16 , South China Sea 19 , 20 , and East Asian continent 21 , 22 for Meiyu-Baiu rainfall. Nevertheless, the dominant source among these major moisture sources for Meiyu-Baiu rainfall is still under debate. The roles of these moisture sources in the extreme Meiyu-Baiu rainfall events have not been systematically clarified. In general, moisture sources of Meiyu-Baiu rainfall in East Asia were mainly investigated by Eulerian moisture tagging 8 , 23 and Lagrangian backward trajectory methods 16 , 24 , which provide quantitative source apportionment. The Lagrangian method, which focuses on the moisture of air particles in the atmosphere from the source region to the sink region, can analyze moisture transport in detail based on particles’ trajectories. Despite its detailed trajectory analysis, the Lagrangian method does not rely on detailed physical equations 25 and neglects all microphysical processes in and below clouds 26 . On the other hand, the Eulerian method is grounded in detailed physical equations and can track moisture from sea surface evaporation until it precipitates to the ground 27 , 28 . The effect of different source apportionment methods on the moisture source contribution during the Meiyu-Baiu season has not yet been stated. Additionally, water stable isotopes have been used to investigate moisture sources and their corresponding hydrological processes 29 – 34 . The isotopic composition of precipitation reflects temperature and humidity conditions in moisture source region during evaporation process 35 – 38 , and subsequent precipitation (rainout effect) 39 , 40 and below-cloud evaporation (below-cloud effect) processes 18 , 41 in the upstream region during moisture transport. The isotopic composition of Meiyu-Baiu rainfall in East Asia has been widely studied based on field observations. Previous studies emphasized the low δ 18 O of Meiyu-Baiu rainfall was attributable to substantial Indian Ocean moisture 13 , 42 , due to strong convection during moisture transport 43 , 44 . In contrast, a few studies indicated the low δ 18 O of Meiyu-Baiu rainfall was affected by intense rainout process in the upstream region of South China Sea 19 , 20 . In addition, some studies proposed the low δ 18 O of Meiyu-Baiu rainfall was associated with cold air mass from polar region 45 , 46 . Recent studies highlighted the high d-excess of Meiyu-Baiu rainfall was attributed to oceanic moisture from South China Sea 41 , 47 and Indian Ocean 18 , with greater below-cloud evaporation during moisture transport. On the other hand, some studies suggested d-excess of oceanic moisture was lower than continental recycling moisture during Meiyu-Baiu period 43 , 48 . Therefore, interpretations on the isotopic characteristics of Meiyu-Baiu rainfall in terms of moisture source differ among studies. A comprehensive understanding of the interannual variability of isotopic composition in Meiyu-Baiu rainfall is still lacking. Some studies have incorporated water vapor and isotopic tracers into general circulation models to investigate moisture sources and isotopic composition of precipitation 49 – 54 . Recent study 18 identified the Asian monsoon moisture had lower δ and higher d-excess with more rainout and below-cloud evaporation than the Pacific Ocean moisture, based on the 2020 heavy Meiyu-Baiu rainfall in Japan. Due to limited computation resources, their case study was too short to capture the interannual variation of moisture source and isotopic composition in Meiyu-Baiu rainfall. Understanding these variations is crucial for comprehending current Meiyu-Baiu rainfall. Moreover, it is essential for reconstructing paleoclimate and improving predictions of future changes in Meiyu-Baiu rainfall. To better understand the interannual variation of Meiyu-Baiu rainfall, this study utilized the IsoRSM to investigate the moisture sources and isotopic composition of Meiyu-Baiu rainfall in southwest Japan during 2004–2023. The objectives of this study are three-fold: (i) to identify the major moisture sources and their corresponding isotopic characteristics of Meiyu-baiu rainfall in southwest Japan during the 20 seasons; (ii) to clarify the interannual variability of isotopic composition and corresponding thermodynamic processes of each moisture source; and (iii) to investigate the role of major moisture sources on the extreme rainfall events. Results and discussion Climatological mean contribution of major moisture sources to Meiyu-Baiu rainfall Previous studies have emphasized the importance of AM moisture in the middle level and NPSH moisture in the low level for heavy Meiyu-Baiu rainfall based on case studies in East Asia 16 , 18 , 41 . Our results showed that AM moisture from the Indian Ocean, the East Asian continent, and the South China Sea (Fig. 1 ) was dominant and contributed 43.7–50.9% of total rainfall and precipitable water (Fig. 2 b, c, Tables 1 , and S1). Large amounts of AM moisture were transported by monsoon southwesterlies to the East China Sea and southwestern Japan (Fig. 3 a and b) in the middle levels above the 305-K potential temperature (θ) surface (Fig. 4 b). The AM moisture resulted in heavy rainfall in the East China Sea and western Kyushu (Fig. 5 b). On the other hand, NPSH moisture transported from Pacific Ocean and Philippine Sea (Fig. 1 ) was moderate and accounted for 27.4–28.6% of total rainfall and precipitable water (Fig. 2 b, c, Tables 1 , and S1). Moderate amounts of NPSH moisture were advected by the NPSH to Kuroshio region and southwestern Japan (Fig. 3 a and c) within the boundary layer below 305-K θ surface (Fig. 4 c). The NPSH moisture contributed to moderated rainfall in Kuroshio regions and eastern Kyushu (Fig. 5 c). Table 1 Contribution (mm) of major moisture sources to the total rainfall in Kyushu for the heavy, light, and normal Meiyu-Baiu seasons. Numbers with brackets are for percentage of major moisture sources to the total rainfall. Total amount of rainfall (mm) with the δ 2 H, δ 18 O, and d-excess (‰) for each category were shown in last four columns. Arithmetic average values for the 20 Meiyu-Baiu seasons are shown in the last row. Category AM moisture NPSH moisture Local moisture Total rainfall δ 2 H δ 18 O d-excess Heavy Meiyu-Baiu season 502.4 (51.4) 282.7 (28.9) 69.0 (7.1) 977.9 -76.8 -10.6 8.2 Light Meiyu-Baiu season 262.3 (53.0) 127.6 (25.8) 45.1 (9.1) 494.5 -69.7 -9.6 7.5 Normal Meiyu-Baiu season 344.7 (49.2) 209.0 (29.8) 57.7 (8.2) 700.4 -72.3 -10.0 7.9 Average 367.3 (50.9) 206.7 (28.6) 57.3 (7.9) 721.9 -72.9 -10.1 7.9 The contribution of major moisture sources is consistent with previous studies 8 , 13 , 18 based on the Eulerian water vapor tagging method. In contrast, some studies underscored the importance of continental moisture 21 and subtropical moisture 16 for Meiyu-Baiu rainfall, using a Lagrangian backward particle trajectory analysis. Previous studies have shown large differences in major moisture contributions between the Eulerian and Lagrangian methods. For moisture contribution to Meiyu-Baiu rainfall, the Eulerian method 8 , 18 , 41 demonstrated a larger contribution (21.8% − 40.1%) of remote moisture, such as the Indian Ocean moisture, compared to the Lagrangian method 16 , 20 – 22 , which showed a contribution of 8.7% − 18.7%. Conversely, the Eulerian method in this study and a previous study 18 showed a smaller contribution (6.4%-7.9%) of local moisture compared to the Lagrangian method 16 , which showed a contribution of 12.7%. This discrepancy is due to the different treatment of re-evaporated vapor below cloud between the two methods. For the below-cloud evaporation process, moisture source information remains unchanged in the Eulerian method but is renewed in the Lagrangian method. This renewal treatment during below-cloud evaporation processes in the Lagrangian method can lead to an underestimation (overestimation) of contribution from distant moisture sources (local moisture). Interannual variability of major moisture sources of Meiyu-Baiu rainfall Recent studies have highlighted that the interannual variability of Meiyu-Baiu rainfall is dominated by the AM moisture 21 , 55 . Our results showed that the heavy Meiyu-Baiu seasons were characterized by a notable enhancement of AM moisture in the East China Sea (Fig. 3 b) at middle levels (Fig. 4 b) and NPSH moisture in Kuroshio region (Fig. 3 c) at low levels (Fig. 4 c). The enhanced supply of AM and NPSH moisture (Fig. 2 c and Table S1 ) can be attributed to the northwestward expansion of the North Pacific subtropical high and the deepening of the East Asian mid-latitude trough near the East China Sea (Fig. 6 c). This is consistent with the results in previous study 56 . It is noteworthy that the augmentation of precipitable water is similar between AM moisture and NPSH moisture, with increases of 2.1 mm and 2.0 mm, respectively (Table S1 ). However, the contribution of AM moisture to total rainfall (240.1 mm) is significantly higher than that of NPSH moisture (155.1 mm) (Table 1 ). This discrepancy can be attributed to the higher precipitation efficiency of AM moisture, which occurs in the middle levels at higher altitudes and lower temperatures. This higher efficiency results in greater rainfall contribution from AM moisture compared to NPSH moisture. Interannual variability of extreme rainfall events during Meiyu-Baiu season Figure 7 displays year to year variations in the number of extreme events, the amount of extreme rainfall, and the major moisture sources of extreme rainfall from Meiyu-Baiu, extratropical cyclones, and typhoon events during the 20 Meiyu-Baiu seasons in Kyushu. Meiyu-Baiu event was the dominant event among the three extreme event types. Both the extreme rainfall amount and the number of extreme events is positively correlated with total Meiyu-Baiu rainfall. The moisture sources for extreme rainfall events are primarily contributed by the AM moisture. Recent study 56 revealed a significant amplification of the interannual variability of Meiyu-Baiu rainfall in recent decades, exhibiting a quasi-quadrennial variation. Our study further emphasizes that the amplified interannual variability is primarily caused by extreme rainfall events (Fig. 7 a and b) with a dominant source of AM moisture (Fig. 7 c). During the Meiyu-Baiu season, large amounts of AM moisture are transported by the Asian monsoonal southwesterlies, moistening the atmosphere in the middle levels. Moreover, the AM moisture in the middle levels increases further during heavy seasons (Fig. 4 b). This enhanced warm and moist air supply in the middle levels with higher precipitation efficiency facilitates deep convection, triggering more extreme rainfall events during heavy Meiyu-Baiu seasons. Extreme Meiyu-Baiu rainfall events have occurred frequently over the past decade and are expected to increase in a warmer climate. This study underscores the significance of Asian monsoon moisture in the middle levels for extreme rainfall events during heavy Meiyu-Baiu seasons. The findings on moisture sources can inform weather forecasting and disaster mitigation strategies for extreme Meiyu-Baiu rainfall, providing valuable insights into controlling and modifying such extreme weather events. Spatial variation of isotopic composition in Meiyu-Baiu rainfall Previous studies attributed the low δ and high d-excess values of Meiyu-Baiu rainfall to different moisture sources 19 , 20 , 43 – 48 . Regarding climatological mean values, our results showed that AM moisture transported by monsoonal southwesterlies from the Indian Ocean, the East Asian continent, and the South China Sea was dominant in the middle levels with low δ 2 H and high d-excess (Fig. 4 b, d, and e). On the other hand, NPSH moisture transported from Pacific Ocean and Philippine Sea had high δ 2 H and low d-excess in the low levels (Fig. 4 c, d, and e). Isotopic composition of Meiyu-Baiu rainfall exhibited interesting spatial variation. Meiyu-Baiu rainfall in East China Sea and western Kyushu exhibited low δ 2 H and high d-excess compared to that in Kuroshio regions and eastern Kyushu (Fig. 5 d and e). This could be attributed to large contribution of AM moisture with relatively low δ 2 H and high d-excess at middle levels (Fig. 4 d and e) to total rainfall in East China Sea and western Kyushu (Fig. 5 b). Conversely, the high δ 2 H and low d-excess of rainfall in Kuroshio regions and eastern Kyushu resulted from more NPSH moisture contribution (Fig. 5 c) with relatively high δ 2 H and low d-excess at middle levels (Fig. 4 d and e). Interannual variability of isotopic composition in Meiyu-Baiu rainfall The isotopic characteristics of major moisture sources for heavy and light seasons were further clarified. AM moisture had lower δ 2 H and higher d-excess in heavy Meiyu-Baiu seasons than in light seasons (Fig. 4 d and e). This could be attributable to large amounts of rainout and below-cloud evaporation in the Asian monsoon regions (Fig. S1 d and g). Additionally, rainout and below-cloud evaporation in the Asian monsoon regions increased further during the heavy seasons (Fig. 6 c). In contrast, the δ 2 H and d-excess of NPSH moisture remained relatively constant between heavy and light seasons (Fig. 4 d and e), with small amounts of rainout and below-cloud evaporation in the upstream regions of Pacific Ocean and Philippine Sea (Fig. S1 d and g). Isotopic composition of rainfall in heavy Meiyu-Baiu seasons exhibited low δ 2 H (-76.8‰) and high d-excess (8.2‰) of rainfall compared to the δ 2 H (-69.7‰) and d-excess (7.5‰) in light seasons (Table 1 , Figs. 2 d, 5 d, and e). This can be explained by lower δ 2 H and higher d-excess of AM moisture with large contribution (51.4%) in heavy seasons (Table 1 and Fig. 4 d and e). In addition, extreme rainfall showed lower δ 2 H (-81.7‰) and high d-excess (8.1‰) with larger contribution (57.8%) of AM moisture in the middle level (Table 2 and Fig. S2). Table 2 Same as Table 1 , but for the extreme rainfall events induced by Meiyu-Baiu front, extratropical cyclone, and typhoon. Category AM moisture NPSH moisture Local moisture Total rainfall δ 2 H δ 18 O d-excess Meiyu-Baiu event 35.7 (57.1) 16.5 (26.2) 3.6 (5.7) 62.7 -83.4 -11.4 8.1 Extratropical cyclone event 43.1 (69.1) 7.0 (11.6) 5.9 (9.7) 61.9 -69.5 -9.7 7.8 Typhoon event 25.8 (44.6) 21.7 (36.7) 5.8 (9.3) 58.5 -76.2 -10.6 8.4 Average 36.0 (57.8) 15.8 (25.2) 3.9 (6.2) 62.5 -81.7 -11.2 8.1 This study systematically investigated the isotopic composition of major moisture sources for 20 Meiyu-Baiu seasons from 2004 to 2023 in southwest Japan. The clarification of interannual variability of isotopic composition of major moisture sources with their isotopic dynamics could contribute to understanding the interannual variability of water cycle in East Asia, as well as to improving seasonal forecasts and near-future predictions of Meiyu-Baiu rainfall several years ahead. Methods Study area Kyushu is the southernmost island of Japan. It is surrounded by the Japan Sea on the north, the East China Sea on the southwest, and the Kuroshio region on the southeast. The islands are occupied by mountain ranges in the central and plain areas on the coast. Kyushu is located at midlatitudes in the Pacific North region along the eastern edge of northeast Asia. It lies in the easternmost region of the East Asian monsoon area. Like other midlatitude coastal regions, the climate of Kyushu is featured by monsoons. The precipitation in Kyushu is principally from rainy season, especially from Meiyu-Baiu period. In June and July, heavy rainfall in Kyushu is most induced by Meiyu-Baiu fronts. Besides, extratropical cyclones and typhoons occasionally approach and bring heavy rainfall to Kyushu. Precipitation sampling and isotopic analyses Rainfall was sampled diurnally at Fukuoka in southwestern Japan during the rainy season from 2021 to 2023. During the 3 rainy seasons, 160 rainfall samples were collected for stable water isotopic analysis using a cavity ring-down spectroscopy isotopic water analyser (L2120-i, Picarro Inc., Sunnyvale, CA, USA) at the Isotope Hydrology Laboratory of Kumamoto University, Japan 57 . Analytical errors were 1.0‰ for δ 2 H and 0.2‰ for δ 18 O. Isotopic Regional Spectral Model (IsoRSM) An isotopic regional spectral model (IsoRSM) 58 was used to simulate Meiyu-Baiu rainfall in Southwestern Japan from 2004 to 2023. The model domain (122.48° E–141.60° E, 23.59° N–39.72° N) covered parts of the East China Sea, Kuroshio region, and Sea of Japan (Fig. 1 ). The IsoRSM utilized a 7.5-km horizontal resolution with 28 vertical sigma levels and 20-s time step for model integration. Simulation was performed from June 1 to July 31 during 2004–2023. The basic design of the IsoRSM was the same as that described in previous study 18 , and the following text is derived from there with minor modifications. The major physics packages in the model include the Chou radiation 59 , cloud microphysics 60 , Relaxed Arakawa–Schubert cumulus convection 61 , and non-local vertical diffusion scheme 62 . Regarding the isotopic processes in the IsoRSM, the isotopic composition ( δ 2 H and δ 18 O) is conserved (no isotopic fractionation occurs) during the dynamic advection processes (moisture transport without water phase change) and terrestrial evapotranspiration processes. Whereas equilibrium fractionation (d-excess defined as δ 2 H − 8 × δ 18 O is conserved) occurs during thermodynamic processes (such as condensation and evaporation) under saturation conditions 63 . Besides, kinetic fractionation is considered for evaporation and isotopic exchange of liquid water with ambient air under unsaturated conditions 64 , and for condensation from vapor to ice under − 20°C under supersaturation conditions 65 . To better understand the moisture sources and moisture transport of the Meiyu-Baiu rainfall in southwestern Japan, we utilized a water-tagging method based on the IsoRSM. In this method, water vapor tracers were used instead of isotopic tracers to track moisture originating from specified source regions 23 . Moisture from each region was tagged during surface evaporation and sublimation, and its origin information was tracked through hydrological processes until it precipitated to the surface. The contribution of each moisture source to the total water vapor or precipitation was then calculated. Notably, both isotopic and water vapor tracers were implemented into the model as purely prognostic variables, with no interference or effect on other physical variables in the model. Four evaporative source regions are defined according to their geographical location, as illustrated in Fig. 1 . The four moisture sources were then grouped into three groups based on large-scale circulation: remote moisture from the Indian Ocean, the South China Sea, and the East Asian Continent; remote moisture from the Pacific Ocean and the Philippine Sea; and local moisture from the surrounding regions of the East China Sea and the Kuroshio region. For convenience, they are called Asian monsoon (AM) moisture, North Pacific subtropical high (NPSH) moisture, and local moisture in order. The initial and lateral boundary conditions for the IsoRSM were derived from the isotopic global spectral model (IsoGSM) 66 . National Centers for Environmental Prediction–Department of Energy Reanalysis 2 (R2) was used as the initial and lateral boundary conditions for the IsoGSM. The surface boundary conditions of sea surface temperature and sea ice concentration for the IsoRSM and IsoGSM were obtained from the National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature V2 (NOAA OISST V2). The fifth generation ECMWF atmospheric reanalysis (ERA5) was used to nudge temperature and horizontal wind fields for a scale larger than 500 km at all layers and all time-steps in the IsoGSM 67 . Grouping of the Meiyu-Baiu rainfall In this study, Meiyu-Baiu rainfall was defined as accumulated rainfall in June and July. The 20 Meiyu-Baiu seasons during 2004–2023 was classified into three categories (heavy Meiyu-Baiu season, light Meiyu-Baiu season, and normal Meiyu-Baiu season) according to the model-simulated Meiyu-Baiu rainfall averaged over Kyushu region. Among the 20 Meiyu-Baiu seasons, six Meiyu-Baiu seasons with the heaviest (lightest) rainfall were selected as heavy (light) Meiyu-Baiu seasons. The remaining seasons were defined as normal Meiyu-Baiu seasons. The IsoRSM-simulated and Radar-observed rainfall amount with category for each Meiyu-Baiu season were shown in Table S2. In addition, extreme rainfall events were identified based on long-term mean (LTM) and standard deviation (SD) of IsoRSM-simulated daily rainfall amount. During the 20 Meiyu-Baiu seasons, the LTM (SD) value is 11.83 (16.72) mm day − 1 . An extreme rainfall event was defined as a day when the rainfall amount was larger than LTM + 2*SD (45.28 mm day − 1 ). Over the 20 Meiyu-Baiu seasons, a total of 71 extreme rainfall events were identified and selected for the events analysis (Table S3). The identified 71 extreme events were then grouped into three types (Meiyu-Baiu event, extratropical cyclone event, and Typhoon event) according to their synoptic weather situations based on daily weather maps obtained from JMA. Meiyu-Baiu event was defined as rainfall associated with the passage of a Meiyu-Baiu stationary front from Kyushu. Extratropical cyclone event was defined as rainfall associated with the passage of an extratropical cyclone with warm and cold fronts from Kyushu. Typhoon event was defined as rainfall associated with the passage of a typhoon from Kyushu. Among the identified 71 extreme events, there are 61 Meiyu-Baiu events, 7 extratropical cyclone events and 3 typhoon events. The date, rainfall amount, and type of each extreme event were shown in Table S3. Validation of the model-simulated results The JMA radar observed rainfall (available from 2006 to 2023) was used to validate the simulated rainfall results. Overall, the simulation shows good correspondence with the JMA radar, regarding spatial distribution and intensity of the Meiyu-Baiu rainfall for each year (Figs. S3, S4, and S5). The correlation coefficient (R) between the observed and simulated Meiyu-Baiu rainfall averaged over Kyushu region is 0.93. In addition, we observed diurnal isotopic composition ( δ 2 H, δ 18 O, and d-excess) of precipitation during rainy season at Fukuoka in southwestern Japan (Fig. 1 ) to validate the simulated isotopic results. The time series of observed and simulated δ 2 H, δ 18 O, and d-excess in the precipitation at Fukuoka are shown in Fig. S6, S7, and S8. The isoRSM reproduced well diurnal variation of the isotopic composition in precipitation at Fukuoka (Fig. S9). The root-mean-square-error (RMSE) between observed and simulated results at Fukuoka is 23.71‰ for δ 2 H, 2.96‰ for δ 18 O, and 3.40‰ for d-excess. The R is 0.66 for δ 2 H, 0.67 for δ 18 O, and 0.49 for d-excess. All correlations at Fukuoka satisfy a 0.1% level of statistical significance for T-test. Both RMSE and R are comparable to previous studies based on the isoRSM 18 , 41 , 68 – 70 . Declarations Competing interests The authors declare no competing interests. Author contributions R.K., and X.L. developed the research concepts. X.L., and K.I. contributed to collect and measure the isotopic data of rainfall. X.L., R.K., and K.Y., contributed to numerical simulation. X.L., and R.K. analyzed and interpreted the data. X.L. wrote the paper and all the authors reviewed the paper and contributed to the final manuscript. Acknowledgments This research was supported by the JSPS KAKENHI, grant numbers JP19H05696 and JP20H00289, JP23K19068, and JP24H00369. Data availability The JRA-55 data is available on the JRA-55 website ( http://jra.kishou.go.jp/JRA-55/index_en.html ). The NOAA OISST V2 data is available at the Earth System Research Laboratory website ( http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html ). 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Sci Online Lett Atmos 9:187–190 Gou J et al (2022) Relationship between precipitation isotopic compositions and synoptic atmospheric circulation patterns in the lower reach of the Yangtze River. J Hydrol (Amst) 605:127289 Xu T et al (2023) Characteristics of water vapor isotopes and moisture sources for short-duration heavy rainfall events in Nanjing, eastern China. J Hydrol (Amst) 622:129731 De Vries AJ, Aemisegger F, Pfahl S, Wernli H (2022) Stable water isotope signals in tropical ice clouds in the West African monsoon simulated with a regional convection-permitting model. Atmos Chem Phys 22:8863–8895 Hu J, Yan Y, Yeung LY, Dee SG (2022) Sublimation Origin of Negative Deuterium Excess Observed in Snow and Ice Samples From McMurdo Dry Valleys and Allan Hills Blue Ice Areas, East Antarctica. J Geophys Research: Atmos 127 Sato T, Nakamura T, Iijima Y, Hiyama T (2022) Enhanced Arctic moisture transport toward Siberia in autumn revealed by tagged moisture transport model experiment. NPJ Clim Atmos Sci 5 Lekshmy PR, Midhun M, Ramesh R (2022) Role of moisture transport from Western Pacific region on water vapor isotopes over the Bay of Bengal. Atmos Res 265 Thurnherr I, Aemisegger F (2022) Disentangling the impact of air-sea interaction and boundary layer cloud formation on stable water isotope signals in the warm sector of a Southern Ocean cyclone. Atmos Chem Phys 22:10353–10373 Yang Y, Yoshimura K (2022) Isotopic Simulation of combustion-derived vapor emission in urban area using regional spectral model. Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 78, I_697-I_702 Guo L et al (2018) The contributions of local and remote atmospheric moisture fluxes to East Asian precipitation and its variability. Clim Dyn 51:4139–4156 Fujiwara K, Kawamura R (2022) Appearance of a Quasi-Quadrennial Variation in Baiu Precipitation in Southern Kyushu, Japan, after the Beginning of This Century. SOLA 18:181–186 Ichiyanagi K et al (2019) Diurnal variation of stable isotopes in rainfall observed at Bengkulu for the YMC-Sumatra 2017. IOP Conf Ser Earth Environ Sci 303:4–11 Yoshimura K, Kanamitsu M, Dettinger M (2010) Regional downscaling for stable water isotopes: A case study of an atmospheric river event. J Geophys Res Atmos 115:1–13 Chou M-D, Suarez MJ (1994) An efficient thermal infrared radiation parameterization for use in general circulation models. Nasa Tech Memo 104606:85 Slingo J (1987) The development and verification of a cloud prediction scheme for the ECMWF model. Q J R Meteorol Soc 113:899–927 Moorthi S, Suarez MJ, Relaxed Arakawa-Schubert (1992) A parameterization of moist convection for general circulation models. Mon Weather Rev 120:978–1002 Hong SY, Pan HL (1998) Convective trigger function for a mass-flux cumulus parameterization scheme. Mon Weather Rev 126:2599–2620 Majoube M (1971) Oxygen-18 and deuterium fractionation between water and steam. J Chim Phys 68:1423–1436 Stewart MK (1975) Stable isotope fractionation due to evaporation and isotopic exchange of falling waterdrops: Applications to atmospheric processes and evaporation of lakes. J Geophys Res 80:1133–1146 Jouzel J, Merlivat L, Deuterium (1984) oxygen 18 in precipitation: Modeling of the isotopic effects during snow formation. J Geophys Res 89:749–757 Yoshimura K, Kanamitsu M, Noone D, Oki T (2008) Historical isotope simulation using Reanalysis atmospheric data. J Geophys Res Atmos 113:1–15 Yoshimura K, Kanamitsu M (2009) Specification of external forcing for regional model integrations. Mon Weather Rev 137:1409–1421 Tanoue M, Ichiyanagi K, Yoshimura K, Shimada J, Hirabayashi Y (2017) Estimation of the isotopic composition and origins of winter precipitation over Japan using a regional isotope circulation model. J Geophys Research: Atmos 122:11621–11637 Li X, Kawamura R, Sugimoto A, Yoshimura K (2021) Estimation of Water Origins within an Explosive Cyclone over the Sea of Japan Using an Isotopic Regional Spectral Model. J Hydrometeorol. 10.1175/JHM-D-21-0027.1 Li X, Kawamura R, Sugimoto A, Yoshimura K (2022) Isotopic composition and moisture sources of precipitation in midlatitude regions characterized by extratropical cyclones’ route. J Hydrol (Amst) 612:128047 Additional Declarations There is NO Competing Interest. Supplementary Files NCSupplementarydata240807.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4870517","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":337774199,"identity":"dad2eb53-6433-4668-b45b-8a7ebe5c3938","order_by":0,"name":"Xiaoyang Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABDklEQVRIie2RMUvEMBiGUwJxiXa9QfAvtBRyg7X9IS4pgbtJcLzhkIhQF39Af4RCQRDcvhJwCnYt3OLtN1w3QRRzUdCl6SqYZ3qH9+FN+BDyeP4gBxgh+IqBjIqPdBcuwaWQHwXD+ZbMrOtWfkXeV0TZ6Fb2aAyvC3U0DW/je0rb7O5amZVlejr8MMKbG63ix2qTJHSyEg+6MMrT7EwOKhhgv1RB3WmW0GglGBglkMqhBLJ5L1VulOkb5c+CtesxBYMyK0XdljyuADLWja4Qrg71XNQdhqiXgrPOrHDHX8JQJ/1mcXxSt405pcxy1s7XL9tlOqh8c4XQhNtU2CZ313dcmD2wKR8vezwez3/jEzXZahBK2PWxAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-1377-4913","institution":"Institute of Industrial Science, The University of Tokyo","correspondingAuthor":true,"prefix":"","firstName":"Xiaoyang","middleName":"","lastName":"Li","suffix":""},{"id":337774200,"identity":"5561d2d5-58cd-476a-a805-9b6cde572a79","order_by":1,"name":"Ryuichi Kawamura","email":"","orcid":"","institution":"Faculty of Science, Kyushu University","correspondingAuthor":false,"prefix":"","firstName":"Ryuichi","middleName":"","lastName":"Kawamura","suffix":""},{"id":337774201,"identity":"2e71a356-e158-45c6-b30e-26bf73dbc5f1","order_by":2,"name":"Kimpei Ichiyanagi","email":"","orcid":"","institution":"Faculty of Advanced Science and Technology, Kumamoto University","correspondingAuthor":false,"prefix":"","firstName":"Kimpei","middleName":"","lastName":"Ichiyanagi","suffix":""},{"id":337774202,"identity":"ad0e3abb-22f1-4df2-bf7c-8217658f276f","order_by":3,"name":"Kei Yoshimura","email":"","orcid":"https://orcid.org/0000-0002-5761-1561","institution":"The University of Tokyo","correspondingAuthor":false,"prefix":"","firstName":"Kei","middleName":"","lastName":"Yoshimura","suffix":""}],"badges":[],"createdAt":"2024-08-06 19:55:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4870517/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4870517/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79166201,"identity":"17827140-59cd-4991-9685-224a5f84498c","added_by":"auto","created_at":"2025-03-25 08:38:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":22427717,"visible":true,"origin":"","legend":"\u003cp\u003ePartition of prespecified tagged-water-source regions indicated by different colors. The domain of the isotopic regional spectral model (IsoRSM) is indicated by the inner dashed black rectangle. The purple contours and black vectors indicate the IsoGSM simulated climatological mean SLP (units of hPa and interval of 2 hPa) and vertically integrated moisture flux (units of kg m\u003csup\u003e-1\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e, and fluxes of less than 100 kg m\u003csup\u003e-1\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e have been suppressed) during the Meiyu-Baiu seasons (June-July) of 2004-2023. The location of sampling sites is shown by blue solid dot at Fukuoka (130.22° E, 33.60° N).\u003c/p\u003e","description":"","filename":"figure1n.png","url":"https://assets-eu.researchsquare.com/files/rs-4870517/v1/79c577ca3dd82cf3de8b3609.png"},{"id":79166203,"identity":"23b9b737-812e-4da7-9de7-65b1f7b3ee1d","added_by":"auto","created_at":"2025-03-25 08:38:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6975756,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Time series of the IsoRSM-simulated Meiyu-Baiu rainfall (purple line), horizontal convergence of vertically integrated moisture flux (green line), and surface turbulent latent heat flux (red line) averaged over the Kyushu region during 2004-2023. (b) Same as (a), but for the major oceanic origins (color bar) of the total precipitable water in mm. Different color shadings indicate the different source regions shown in Fig. 1. (c) Same as (b), but for accumulated Meiyu-Baiu rainfall in mm. (d) Same as (c), but for \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003eH (black line) and d-excess (red line) of Meiyu-Baiu rainfall.\u003c/p\u003e","description":"","filename":"figure2n.png","url":"https://assets-eu.researchsquare.com/files/rs-4870517/v1/f53f20216a872c3bf7a26dbf.png"},{"id":79166205,"identity":"57d39a5c-e333-4524-9084-5165a5c3e1ba","added_by":"auto","created_at":"2025-03-25 08:38:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":33661971,"visible":true,"origin":"","legend":"\u003cp\u003e(a) (Upper panel) Spatial distributions of the IsoGSM simulated total precipitable water (shaded, unit is mm), SLP (red contours at 1006, 1008 and 1010 hPa), and vertically integrated moisture flux (units of kg m\u003csup\u003e-1\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e, and fluxes of less than 200 kg m\u003csup\u003e-1\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e have been suppressed) for the heavy Meiyu-Baiu seasons; (middle panel) same as (upper panel), but for the light Meiyu-Baiu seasons; (lower panel) same as (upper panel), but for the difference between the heavy and light Meiyu-Baiu seasons. Dashed and solid red contours indicate SLP difference at -0.5, 0.5, and 1.0 hPa. Vertically integrated moisture fluxes of less than 20 kg m\u003csup\u003e-1\u003c/sup\u003e s\u003csup\u003e-1\u003c/sup\u003e have been suppressed. (b) same as (a), but for Asian monsoon (AM) moisture. (c) same as (a), but for North Pacific Subtropical High (NPSH) moisture. (d) same as (a), but for local moisture.\u003c/p\u003e","description":"","filename":"figure3n.png","url":"https://assets-eu.researchsquare.com/files/rs-4870517/v1/a747d6d3b686936b64f17246.png"},{"id":79166202,"identity":"9fb314fd-6f93-4bf3-82f7-dba6ad57bdda","added_by":"auto","created_at":"2025-03-25 08:38:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":25594090,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Latitude-height vertical distributions of the IsoRSM simulated total specific humidity (shaded, unit is g kg\u003csup\u003e-1\u003c/sup\u003e), potential temperature θ (purple contours, unit of K and interval of 5 K), and zonal and vertical wind (vectors, unit is m s\u003csup\u003e-1\u003c/sup\u003e for zonal wind and Pa s\u003csup\u003e-1\u003c/sup\u003e for vertical wind) averaged over zonal zone of Kyushu region between 129◦ E and 132◦ E for the heavy Meiyu-Baiu seasons (upper panels), light Meiyu-Baiu seasons (middle panels), and the 20 seasons (lower panels); the red contours in the lower panel indicate the difference of total specific humidity between the heavy and light seasons. (b) Same as (a), but for the Asian Monsoon (AM) moisture. (c) Same as (b), but for the North Pacific Subtropical High (NPSH) moisture. (d) Same as (a), but for \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003eH (‰) of vapor. The purple contours in the lower panel indicate the difference of \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003eH (‰) between the heavy and light seasons. (e) Same as (a), but for d-excess (‰) of vapor. The red contours in the lower panel indicate the difference of d-excess (‰) between the heavy and light seasons.\u003c/p\u003e","description":"","filename":"figure4n.png","url":"https://assets-eu.researchsquare.com/files/rs-4870517/v1/61b857f02237ffe7d4203668.png"},{"id":79166200,"identity":"237fda7f-7d36-4999-a989-46315da8fb15","added_by":"auto","created_at":"2025-03-25 08:38:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":34951303,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Spatial distributions of the IsoRSM simulated total Meiyu-Baiu rainfall (shaded, unit is mm) averaged during the heavy seasons (upper panel), light seasons (middle panel), and the 20 seasons (lower panel). The red contours in the lower panel indicate the rainfall difference between heavy and light seasons at 200 mm. (b) same as (a), but for rainfall from Asian monsoon (AM) moisture. (c) same as (a), but for North Pacific Subtropical High (NPSH) moisture. (d) same as (a), but for \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003eH in Meiyu-Baiu rainfall. The purple contours in the lower panel indicate the \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003eH difference at -10 ‰. (e) same as (a), but for d-excess in Meiyu-Baiu rainfall. The red contours in the lower panel indicate the d-excess difference at 1 ‰.\u003c/p\u003e","description":"","filename":"figure5n.png","url":"https://assets-eu.researchsquare.com/files/rs-4870517/v1/33c9ea1b9d7a421f6619c10a.png"},{"id":79166212,"identity":"293758c1-007f-4abc-9bbf-141651797a37","added_by":"auto","created_at":"2025-03-25 08:38:13","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":18480538,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the IsoGSM simulated mean specific humidity from the Asian monsoon region (blue contour with shading, unit of g kg\u003csup\u003e−1\u003c/sup\u003e), and the North Pacific subtropical high region (red contour with shading, unit of g kg\u003csup\u003e−1\u003c/sup\u003e), geopotential height (green contours, unit of gpm and interval of 20 gpm), and moisture flux (black vectors, unit of g kg\u003csup\u003e−1\u003c/sup\u003e m s\u003csup\u003e−1\u003c/sup\u003e) on an 700 hPa isobaric surface for the heavy Meiyu-Baiu rainfall seasons (a), the light rainfall seasons (b), and the difference between the heavy and light seasons (c). Gray shading indicates Tibetan Plateau with elevation higher than 3000 m above sea level. Yellow shading indicates Meiyu-Baiu rainfall larger than 600 mm for (a) and (b) and larger than 100 mm for (c).\u003c/p\u003e","description":"","filename":"figure6n.png","url":"https://assets-eu.researchsquare.com/files/rs-4870517/v1/7a01b814e63feaf4bce9c79c.png"},{"id":79167230,"identity":"bd76757a-ebb3-4e66-8396-388b87a79d1f","added_by":"auto","created_at":"2025-03-25 08:46:13","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":3410243,"visible":true,"origin":"","legend":"\u003cp\u003e(a) Time series of extreme event number from Meiyu-Baiu (black shading), extratropical cyclone (dark gray shading), and typhoon events (light gray shading) in Kyushu during 2004-2023. Black line indicates the Meiyu-Baiu seasonal rainfall amount. (b) Same as (a), but for rainfall amount of extreme event. (c) Same as (a), but for the major water origins (color bar) of extreme rainfall.\u003c/p\u003e","description":"","filename":"figure7n.png","url":"https://assets-eu.researchsquare.com/files/rs-4870517/v1/a9134141c8222074e5ec2a4a.png"},{"id":79166198,"identity":"9c88d035-0685-40d2-9d64-50027fb4a383","added_by":"auto","created_at":"2025-03-25 08:38:11","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":1394457,"visible":true,"origin":"","legend":"","description":"","filename":"NCSupplementarydata240807.docx","url":"https://assets-eu.researchsquare.com/files/rs-4870517/v1/731f21f280bc7d23c43b7220.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Moisture sources and isotopic composition of Meiyu-Baiu rainfall in southwestern Japan from 2004 to 2023","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMeiyu-Baiu rainfall is an important component of summer rainfall in East Asia and is characterized by a midlatitude front with a tropical convective nature\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The interannual variability of Meiyu-Baiu rainfall, in terms of its position and intensity, is closely linked to floods and droughts, leading to significant socioeconomic losses in East Asia\u003csup\u003e\u003cspan additionalcitationids=\"CR4\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Generally, the variability of Meiyu-Baiu rainfall is influenced by the Asian monsoon (AM) southwesterlies\u003csup\u003e\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e–\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, the North Pacific subtropical high (NPSH)\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, and the East Asian mid-latitude westerlies\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eDuring the Meiyu-Baiu period in early summer, large amounts of moisture are transported to East Asia by monsoonal southwesterlies from the Indian Ocean\u003csup\u003e\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e and by the NPSH from the Pacific Ocean\u003csup\u003e\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Previous studies have highlighted the importance of moisture from the Indian Ocean\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e, Pacific Ocean\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, South China Sea\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and East Asian continent\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e for Meiyu-Baiu rainfall. Nevertheless, the dominant source among these major moisture sources for Meiyu-Baiu rainfall is still under debate. The roles of these moisture sources in the extreme Meiyu-Baiu rainfall events have not been systematically clarified.\u003c/p\u003e \u003cp\u003eIn general, moisture sources of Meiyu-Baiu rainfall in East Asia were mainly investigated by Eulerian moisture tagging\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e and Lagrangian backward trajectory methods\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, which provide quantitative source apportionment. The Lagrangian method, which focuses on the moisture of air particles in the atmosphere from the source region to the sink region, can analyze moisture transport in detail based on particles’ trajectories. Despite its detailed trajectory analysis, the Lagrangian method does not rely on detailed physical equations\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e and neglects all microphysical processes in and below clouds\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. On the other hand, the Eulerian method is grounded in detailed physical equations and can track moisture from sea surface evaporation until it precipitates to the ground\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e,\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. The effect of different source apportionment methods on the moisture source contribution during the Meiyu-Baiu season has not yet been stated.\u003c/p\u003e \u003cp\u003eAdditionally, water stable isotopes have been used to investigate moisture sources and their corresponding hydrological processes\u003csup\u003e\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e–\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. The isotopic composition of precipitation reflects temperature and humidity conditions in moisture source region during evaporation process\u003csup\u003e\u003cspan additionalcitationids=\"CR36 CR37\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e–\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e, and subsequent precipitation (rainout effect)\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e and below-cloud evaporation (below-cloud effect) processes\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e in the upstream region during moisture transport. The isotopic composition of Meiyu-Baiu rainfall in East Asia has been widely studied based on field observations.\u003c/p\u003e \u003cp\u003ePrevious studies emphasized the low \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO of Meiyu-Baiu rainfall was attributable to substantial Indian Ocean moisture\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, due to strong convection during moisture transport\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In contrast, a few studies indicated the low \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO of Meiyu-Baiu rainfall was affected by intense rainout process in the upstream region of South China Sea\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. In addition, some studies proposed the low \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO of Meiyu-Baiu rainfall was associated with cold air mass from polar region\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e,\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent studies highlighted the high d-excess of Meiyu-Baiu rainfall was attributed to oceanic moisture from South China Sea\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e and Indian Ocean\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, with greater below-cloud evaporation during moisture transport. On the other hand, some studies suggested d-excess of oceanic moisture was lower than continental recycling moisture during Meiyu-Baiu period\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e,\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Therefore, interpretations on the isotopic characteristics of Meiyu-Baiu rainfall in terms of moisture source differ among studies. A comprehensive understanding of the interannual variability of isotopic composition in Meiyu-Baiu rainfall is still lacking.\u003c/p\u003e \u003cp\u003eSome studies have incorporated water vapor and isotopic tracers into general circulation models to investigate moisture sources and isotopic composition of precipitation\u003csup\u003e\u003cspan additionalcitationids=\"CR50 CR51 CR52 CR53\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e–\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. Recent study\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e identified the Asian monsoon moisture had lower δ and higher d-excess with more rainout and below-cloud evaporation than the Pacific Ocean moisture, based on the 2020 heavy Meiyu-Baiu rainfall in Japan. Due to limited computation resources, their case study was too short to capture the interannual variation of moisture source and isotopic composition in Meiyu-Baiu rainfall. Understanding these variations is crucial for comprehending current Meiyu-Baiu rainfall. Moreover, it is essential for reconstructing paleoclimate and improving predictions of future changes in Meiyu-Baiu rainfall.\u003c/p\u003e \u003cp\u003eTo better understand the interannual variation of Meiyu-Baiu rainfall, this study utilized the IsoRSM to investigate the moisture sources and isotopic composition of Meiyu-Baiu rainfall in southwest Japan during 2004–2023. The objectives of this study are three-fold: (i) to identify the major moisture sources and their corresponding isotopic characteristics of Meiyu-baiu rainfall in southwest Japan during the 20 seasons; (ii) to clarify the interannual variability of isotopic composition and corresponding thermodynamic processes of each moisture source; and (iii) to investigate the role of major moisture sources on the extreme rainfall events.\u003c/p\u003e "},{"header":"Results and discussion","content":"\u003cp\u003e \u003cb\u003eClimatological mean contribution of major moisture sources to Meiyu-Baiu rainfall\u003c/b\u003e \u003c/p\u003e\u003cp\u003ePrevious studies have emphasized the importance of AM moisture in the middle level and NPSH moisture in the low level for heavy Meiyu-Baiu rainfall based on case studies in East Asia\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. Our results showed that AM moisture from the Indian Ocean, the East Asian continent, and the South China Sea (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was dominant and contributed 43.7–50.9% of total rainfall and precipitable water (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and S1). Large amounts of AM moisture were transported by monsoon southwesterlies to the East China Sea and southwestern Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and b) in the middle levels above the 305-K potential temperature (θ) surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). The AM moisture resulted in heavy rainfall in the East China Sea and western Kyushu (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). On the other hand, NPSH moisture transported from Pacific Ocean and Philippine Sea (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) was moderate and accounted for 27.4–28.6% of total rainfall and precipitable water (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb, c, Tables\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and S1). Moderate amounts of NPSH moisture were advected by the NPSH to Kuroshio region and southwestern Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea and c) within the boundary layer below 305-K θ surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The NPSH moisture contributed to moderated rainfall in Kuroshio regions and eastern Kyushu (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eContribution (mm) of major moisture sources to the total rainfall in Kyushu for the heavy, light, and normal Meiyu-Baiu seasons. Numbers with brackets are for percentage of major moisture sources to the total rainfall. Total amount of rainfall (mm) with the \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH, \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO, and d-excess (‰) for each category were shown in last four columns. Arithmetic average values for the 20 Meiyu-Baiu seasons are shown in the last row.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003cp\u003emoisture\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNPSH\u003c/p\u003e \u003cp\u003emoisture\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003cp\u003emoisture\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003erainfall\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ed-excess\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy Meiyu-Baiu season\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e502.4 (51.4)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e282.7 (28.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e69.0 (7.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e977.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-76.8\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-10.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.2\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight Meiyu-Baiu season\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e262.3 (53.0)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e127.6 (25.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e45.1 (9.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e494.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-69.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-9.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNormal Meiyu-Baiu season\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e344.7 (49.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e209.0 (29.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.7 (8.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e700.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-72.3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-10.0\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e367.3 (50.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e206.7 (28.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57.3 (7.9)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e721.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-72.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-10.1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.9\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eThe contribution of major moisture sources is consistent with previous studies\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e based on the Eulerian water vapor tagging method. In contrast, some studies underscored the importance of continental moisture\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e and subtropical moisture\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e for Meiyu-Baiu rainfall, using a Lagrangian backward particle trajectory analysis. Previous studies have shown large differences in major moisture contributions between the Eulerian and Lagrangian methods. For moisture contribution to Meiyu-Baiu rainfall, the Eulerian method\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e demonstrated a larger contribution (21.8% − 40.1%) of remote moisture, such as the Indian Ocean moisture, compared to the Lagrangian method \u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e–\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, which showed a contribution of 8.7% − 18.7%. Conversely, the Eulerian method in this study and a previous study\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e showed a smaller contribution (6.4%-7.9%) of local moisture compared to the Lagrangian method\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e, which showed a contribution of 12.7%.\u003c/p\u003e\u003cp\u003eThis discrepancy is due to the different treatment of re-evaporated vapor below cloud between the two methods. For the below-cloud evaporation process, moisture source information remains unchanged in the Eulerian method but is renewed in the Lagrangian method. This renewal treatment during below-cloud evaporation processes in the Lagrangian method can lead to an underestimation (overestimation) of contribution from distant moisture sources (local moisture).\u003c/p\u003e\u003cp\u003e \u003cb\u003eInterannual variability of major moisture sources of Meiyu-Baiu rainfall\u003c/b\u003e \u003c/p\u003e\u003cp\u003eRecent studies have highlighted that the interannual variability of Meiyu-Baiu rainfall is dominated by the AM moisture\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. Our results showed that the heavy Meiyu-Baiu seasons were characterized by a notable enhancement of AM moisture in the East China Sea (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) at middle levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb) and NPSH moisture in Kuroshio region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) at low levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec). The enhanced supply of AM and NPSH moisture (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) can be attributed to the northwestward expansion of the North Pacific subtropical high and the deepening of the East Asian mid-latitude trough near the East China Sea (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). This is consistent with the results in previous study\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eIt is noteworthy that the augmentation of precipitable water is similar between AM moisture and NPSH moisture, with increases of 2.1 mm and 2.0 mm, respectively (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). However, the contribution of AM moisture to total rainfall (240.1 mm) is significantly higher than that of NPSH moisture (155.1 mm) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This discrepancy can be attributed to the higher precipitation efficiency of AM moisture, which occurs in the middle levels at higher altitudes and lower temperatures. This higher efficiency results in greater rainfall contribution from AM moisture compared to NPSH moisture.\u003c/p\u003e\u003cp\u003e \u003cb\u003eInterannual variability of extreme rainfall events during Meiyu-Baiu season\u003c/b\u003e \u003c/p\u003e\u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e displays year to year variations in the number of extreme events, the amount of extreme rainfall, and the major moisture sources of extreme rainfall from Meiyu-Baiu, extratropical cyclones, and typhoon events during the 20 Meiyu-Baiu seasons in Kyushu. Meiyu-Baiu event was the dominant event among the three extreme event types. Both the extreme rainfall amount and the number of extreme events is positively correlated with total Meiyu-Baiu rainfall. The moisture sources for extreme rainfall events are primarily contributed by the AM moisture.\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cp\u003eRecent study\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e revealed a significant amplification of the interannual variability of Meiyu-Baiu rainfall in recent decades, exhibiting a quasi-quadrennial variation. Our study further emphasizes that the amplified interannual variability is primarily caused by extreme rainfall events (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ea and b) with a dominant source of AM moisture (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003ec). During the Meiyu-Baiu season, large amounts of AM moisture are transported by the Asian monsoonal southwesterlies, moistening the atmosphere in the middle levels. Moreover, the AM moisture in the middle levels increases further during heavy seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb). This enhanced warm and moist air supply in the middle levels with higher precipitation efficiency facilitates deep convection, triggering more extreme rainfall events during heavy Meiyu-Baiu seasons.\u003c/p\u003e\u003cp\u003eExtreme Meiyu-Baiu rainfall events have occurred frequently over the past decade and are expected to increase in a warmer climate. This study underscores the significance of Asian monsoon moisture in the middle levels for extreme rainfall events during heavy Meiyu-Baiu seasons. The findings on moisture sources can inform weather forecasting and disaster mitigation strategies for extreme Meiyu-Baiu rainfall, providing valuable insights into controlling and modifying such extreme weather events.\u003c/p\u003e\u003cp\u003e \u003cb\u003eSpatial variation of isotopic composition in Meiyu-Baiu rainfall\u003c/b\u003e \u003c/p\u003e\u003cp\u003ePrevious studies attributed the low \u003cem\u003eδ\u003c/em\u003e and high d-excess values of Meiyu-Baiu rainfall to different moisture sources\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e,\u003cspan additionalcitationids=\"CR44 CR45 CR46 CR47\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e–\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Regarding climatological mean values, our results showed that AM moisture transported by monsoonal southwesterlies from the Indian Ocean, the East Asian continent, and the South China Sea was dominant in the middle levels with low \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and high d-excess (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb, d, and e). On the other hand, NPSH moisture transported from Pacific Ocean and Philippine Sea had high \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and low d-excess in the low levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ec, d, and e).\u003c/p\u003e\u003cp\u003eIsotopic composition of Meiyu-Baiu rainfall exhibited interesting spatial variation. Meiyu-Baiu rainfall in East China Sea and western Kyushu exhibited low \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and high d-excess compared to that in Kuroshio regions and eastern Kyushu (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed and e). This could be attributed to large contribution of AM moisture with relatively low \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and high d-excess at middle levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed and e) to total rainfall in East China Sea and western Kyushu (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Conversely, the high \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and low d-excess of rainfall in Kuroshio regions and eastern Kyushu resulted from more NPSH moisture contribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ec) with relatively high \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and low d-excess at middle levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed and e).\u003c/p\u003e\u003cp\u003e \u003cb\u003eInterannual variability of isotopic composition in Meiyu-Baiu rainfall\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe isotopic characteristics of major moisture sources for heavy and light seasons were further clarified. AM moisture had lower \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and higher d-excess in heavy Meiyu-Baiu seasons than in light seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed and e). This could be attributable to large amounts of rainout and below-cloud evaporation in the Asian monsoon regions (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ed and g). Additionally, rainout and below-cloud evaporation in the Asian monsoon regions increased further during the heavy seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003ec). In contrast, the \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and d-excess of NPSH moisture remained relatively constant between heavy and light seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed and e), with small amounts of rainout and below-cloud evaporation in the upstream regions of Pacific Ocean and Philippine Sea (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003ed and g).\u003c/p\u003e\u003cp\u003eIsotopic composition of rainfall in heavy Meiyu-Baiu seasons exhibited low \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH (-76.8‰) and high d-excess (8.2‰) of rainfall compared to the \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH (-69.7‰) and d-excess (7.5‰) in light seasons (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ed, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed, and e). This can be explained by lower \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and higher d-excess of AM moisture with large contribution (51.4%) in heavy seasons (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ed and e). In addition, extreme rainfall showed lower \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH (-81.7‰) and high d-excess (8.1‰) with larger contribution (57.8%) of AM moisture in the middle level (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig. S2).\u003c/p\u003e\u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSame as Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, but for the extreme rainfall events induced by Meiyu-Baiu front, extratropical cyclone, and typhoon.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAM\u003c/p\u003e \u003cp\u003emoisture\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNPSH\u003c/p\u003e \u003cp\u003emoisture\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocal\u003c/p\u003e \u003cp\u003emoisture\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003erainfall\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ed-excess\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMeiyu-Baiu event\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e35.7 (57.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.5 (26.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.6 (5.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-83.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-11.4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExtratropical cyclone event\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e43.1 (69.1)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.0 (11.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.9 (9.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.9\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-69.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-9.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTyphoon event\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.8 (44.6)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21.7 (36.7)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.8 (9.3)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e58.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-76.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-10.6\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.4\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36.0 (57.8)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15.8 (25.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.9 (6.2)\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e62.5\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-81.7\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-11.2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8.1\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThis study systematically investigated the isotopic composition of major moisture sources for 20 Meiyu-Baiu seasons from 2004 to 2023 in southwest Japan. The clarification of interannual variability of isotopic composition of major moisture sources with their isotopic dynamics could contribute to understanding the interannual variability of water cycle in East Asia, as well as to improving seasonal forecasts and near-future predictions of Meiyu-Baiu rainfall several years ahead.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e \u003cb\u003eStudy area\u003c/b\u003e \u003c/p\u003e\u003cp\u003eKyushu is the southernmost island of Japan. It is surrounded by the Japan Sea on the north, the East China Sea on the southwest, and the Kuroshio region on the southeast. The islands are occupied by mountain ranges in the central and plain areas on the coast.\u003c/p\u003e\u003cp\u003eKyushu is located at midlatitudes in the Pacific North region along the eastern edge of northeast Asia. It lies in the easternmost region of the East Asian monsoon area. Like other midlatitude coastal regions, the climate of Kyushu is featured by monsoons. The precipitation in Kyushu is principally from rainy season, especially from Meiyu-Baiu period. In June and July, heavy rainfall in Kyushu is most induced by Meiyu-Baiu fronts. Besides, extratropical cyclones and typhoons occasionally approach and bring heavy rainfall to Kyushu.\u003c/p\u003e\u003cp\u003e \u003cb\u003ePrecipitation sampling and isotopic analyses\u003c/b\u003e \u003c/p\u003e\u003cp\u003eRainfall was sampled diurnally at Fukuoka in southwestern Japan during the rainy season from 2021 to 2023. During the 3 rainy seasons, 160 rainfall samples were collected for stable water isotopic analysis using a cavity ring-down spectroscopy isotopic water analyser (L2120-i, Picarro Inc., Sunnyvale, CA, USA) at the Isotope Hydrology Laboratory of Kumamoto University, Japan\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. Analytical errors were 1.0‰ for \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and 0.2‰ for \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO.\u003c/p\u003e\u003cp\u003e \u003cb\u003eIsotopic Regional Spectral Model (IsoRSM)\u003c/b\u003e \u003c/p\u003e\u003cp\u003eAn isotopic regional spectral model (IsoRSM)\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e was used to simulate Meiyu-Baiu rainfall in Southwestern Japan from 2004 to 2023. The model domain (122.48° E–141.60° E, 23.59° N–39.72° N) covered parts of the East China Sea, Kuroshio region, and Sea of Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The IsoRSM utilized a 7.5-km horizontal resolution with 28 vertical sigma levels and 20-s time step for model integration. Simulation was performed from June 1 to July 31 during 2004–2023. The basic design of the IsoRSM was the same as that described in previous study\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, and the following text is derived from there with minor modifications.\u003c/p\u003e\u003cp\u003eThe major physics packages in the model include the Chou radiation\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e, cloud microphysics\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e, Relaxed Arakawa–Schubert cumulus convection\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e, and non-local vertical diffusion scheme\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Regarding the isotopic processes in the IsoRSM, the isotopic composition (\u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO) is conserved (no isotopic fractionation occurs) during the dynamic advection processes (moisture transport without water phase change) and terrestrial evapotranspiration processes. Whereas equilibrium fractionation (d-excess defined as \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003eH − 8 × \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e18\u003c/sup\u003eO is conserved) occurs during thermodynamic processes (such as condensation and evaporation) under saturation conditions\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u003c/sup\u003e. Besides, kinetic fractionation is considered for evaporation and isotopic exchange of liquid water with ambient air under unsaturated conditions\u003csup\u003e\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e, and for condensation from vapor to ice under − 20°C under supersaturation conditions\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eTo better understand the moisture sources and moisture transport of the Meiyu-Baiu rainfall in southwestern Japan, we utilized a water-tagging method based on the IsoRSM. In this method, water vapor tracers were used instead of isotopic tracers to track moisture originating from specified source regions\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Moisture from each region was tagged during surface evaporation and sublimation, and its origin information was tracked through hydrological processes until it precipitated to the surface. The contribution of each moisture source to the total water vapor or precipitation was then calculated. Notably, both isotopic and water vapor tracers were implemented into the model as purely prognostic variables, with no interference or effect on other physical variables in the model.\u003c/p\u003e\u003cp\u003eFour evaporative source regions are defined according to their geographical location, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The four moisture sources were then grouped into three groups based on large-scale circulation: remote moisture from the Indian Ocean, the South China Sea, and the East Asian Continent; remote moisture from the Pacific Ocean and the Philippine Sea; and local moisture from the surrounding regions of the East China Sea and the Kuroshio region. For convenience, they are called Asian monsoon (AM) moisture, North Pacific subtropical high (NPSH) moisture, and local moisture in order.\u003c/p\u003e\u003cp\u003eThe initial and lateral boundary conditions for the IsoRSM were derived from the isotopic global spectral model (IsoGSM)\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. National Centers for Environmental Prediction–Department of Energy Reanalysis 2 (R2) was used as the initial and lateral boundary conditions for the IsoGSM. The surface boundary conditions of sea surface temperature and sea ice concentration for the IsoRSM and IsoGSM were obtained from the National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature V2 (NOAA OISST V2). The fifth generation ECMWF atmospheric reanalysis (ERA5) was used to nudge temperature and horizontal wind fields for a scale larger than 500 km at all layers and all time-steps in the IsoGSM\u003csup\u003e\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e \u003cb\u003eGrouping of the Meiyu-Baiu rainfall\u003c/b\u003e \u003c/p\u003e\u003cp\u003eIn this study, Meiyu-Baiu rainfall was defined as accumulated rainfall in June and July. The 20 Meiyu-Baiu seasons during 2004–2023 was classified into three categories (heavy Meiyu-Baiu season, light Meiyu-Baiu season, and normal Meiyu-Baiu season) according to the model-simulated Meiyu-Baiu rainfall averaged over Kyushu region. Among the 20 Meiyu-Baiu seasons, six Meiyu-Baiu seasons with the heaviest (lightest) rainfall were selected as heavy (light) Meiyu-Baiu seasons. The remaining seasons were defined as normal Meiyu-Baiu seasons. The IsoRSM-simulated and Radar-observed rainfall amount with category for each Meiyu-Baiu season were shown in Table S2.\u003c/p\u003e\u003cp\u003eIn addition, extreme rainfall events were identified based on long-term mean (LTM) and standard deviation (SD) of IsoRSM-simulated daily rainfall amount. During the 20 Meiyu-Baiu seasons, the LTM (SD) value is 11.83 (16.72) mm day\u003csup\u003e− 1\u003c/sup\u003e. An extreme rainfall event was defined as a day when the rainfall amount was larger than LTM + 2*SD (45.28 mm day\u003csup\u003e− 1\u003c/sup\u003e). Over the 20 Meiyu-Baiu seasons, a total of 71 extreme rainfall events were identified and selected for the events analysis (Table S3).\u003c/p\u003e\u003cp\u003eThe identified 71 extreme events were then grouped into three types (Meiyu-Baiu event, extratropical cyclone event, and Typhoon event) according to their synoptic weather situations based on daily weather maps obtained from JMA. Meiyu-Baiu event was defined as rainfall associated with the passage of a Meiyu-Baiu stationary front from Kyushu. Extratropical cyclone event was defined as rainfall associated with the passage of an extratropical cyclone with warm and cold fronts from Kyushu. Typhoon event was defined as rainfall associated with the passage of a typhoon from Kyushu. Among the identified 71 extreme events, there are 61 Meiyu-Baiu events, 7 extratropical cyclone events and 3 typhoon events. The date, rainfall amount, and type of each extreme event were shown in Table S3.\u003c/p\u003e\u003cp\u003e \u003cb\u003eValidation of the model-simulated results\u003c/b\u003e \u003c/p\u003e\u003cp\u003eThe JMA radar observed rainfall (available from 2006 to 2023) was used to validate the simulated rainfall results. Overall, the simulation shows good correspondence with the JMA radar, regarding spatial distribution and intensity of the Meiyu-Baiu rainfall for each year (Figs. S3, S4, and S5). The correlation coefficient (R) between the observed and simulated Meiyu-Baiu rainfall averaged over Kyushu region is 0.93.\u003c/p\u003e\u003cp\u003eIn addition, we observed diurnal isotopic composition (\u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH, \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO, and d-excess) of precipitation during rainy season at Fukuoka in southwestern Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) to validate the simulated isotopic results. The time series of observed and simulated \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH, \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO, and d-excess in the precipitation at Fukuoka are shown in Fig. S6, S7, and S8.\u003c/p\u003e\u003cp\u003eThe isoRSM reproduced well diurnal variation of the isotopic composition in precipitation at Fukuoka (Fig. S9). The root-mean-square-error (RMSE) between observed and simulated results at Fukuoka is 23.71‰ for \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH, 2.96‰ for \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO, and 3.40‰ for d-excess. The R is 0.66 for \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH, 0.67 for \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003eO, and 0.49 for d-excess. All correlations at Fukuoka satisfy a 0.1% level of statistical significance for T-test. Both RMSE and R are comparable to previous studies based on the isoRSM\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e,\u003cspan additionalcitationids=\"CR69\" citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e–\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e \u003cp\u003eR.K., and X.L. developed the research concepts. X.L., and K.I. contributed to collect and measure the isotopic data of rainfall. X.L., R.K., and K.Y., contributed to numerical simulation. X.L., and R.K. analyzed and interpreted the data. X.L. wrote the paper and all the authors reviewed the paper and contributed to the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThis research was supported by the JSPS KAKENHI, grant numbers JP19H05696 and JP20H00289, JP23K19068, and JP24H00369.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe JRA-55 data is available on the JRA-55 website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://jra.kishou.go.jp/JRA-55/index_en.html\u003c/span\u003e\u003cspan address=\"http://jra.kishou.go.jp/JRA-55/index_en.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The NOAA OISST V2 data is available at the Earth System Research Laboratory website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html\u003c/span\u003e\u003cspan address=\"http://www.esrl.noaa.gov/psd/data/gridded/data.noaa.oisst.v2.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The JMA radar data is collected and distributed by Research Institute for Sustainable Humanosphere, Kyoto University (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://database.rish.kyoto-u.ac.jp/arch/jmadata/data/jma-radar/synthetic/original/\u003c/span\u003e\u003cspan address=\"http://database.rish.kyoto-u.ac.jp/arch/jmadata/data/jma-radar/synthetic/original/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003ch2\u003eCode availability\u003c/h2\u003e \u003cp\u003eThe source code of isotopic global and regional model should be addressed to Kei Yoshimura.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNinomiya K (1987) The early summer rainy season (Baiu) over Japan. 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J Hydrol (Amst) 612:128047\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-4870517/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4870517/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTo better understand the underlying atmospheric processes responsible for interannual variability of Meiy-Baiu rainfall, we utilized an isotopic regional spectral model to investigate the moisture sources and isotopic composition of Meiyu-Baiu rainfall in southwestern Japan from 2004 to 2023. Asian Monsoon (AM) moisture, transported by the monsoonal southwesterlies in middle levels, contributed 50.9% of the total rainfall with low \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and high d-excess. Conversely, North Pacific subtropical high (NPSH) moisture in low levels accounted for 28.6% of the total rainfall with high \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and low d-excess. AM moisture during heavy rainfall seasons exhibited lower \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and higher d-excess, due to more rainout and below-cloud evaporation. In contrast, the isotopic signals of NPSH moisture were relatively consistent between heavy and light seasons. Extreme rainfall showed lower \u003cem\u003eδ\u003c/em\u003e\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003eH and higher d-excess with more contribution (57.8%) from AM moisture at middle levels with high precipitation efficiency, which facilitates deep convection, triggering more extreme rainfall events during heavy seasons. The study highlights importance of AM moisture for extreme Meiyu-Baiu rainfall in East Asia. The findings providing valuable insights into understanding the interannual variability of water cycle in East Asia, as well as to improving seasonal forecasts and near-future predictions of Meiyu-Baiu rainfall.\u003c/p\u003e","manuscriptTitle":"Moisture sources and isotopic composition of Meiyu-Baiu rainfall in southwestern Japan from 2004 to 2023","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 08:38:06","doi":"10.21203/rs.3.rs-4870517/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"700eedac-1516-4a14-8abd-25613131d54d","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":35780500,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric chemistry"},{"id":35780501,"name":"Earth and environmental sciences/Climate sciences/Hydrology"},{"id":35780502,"name":"Earth and environmental sciences/Climate sciences/Atmospheric science/Atmospheric dynamics"}],"tags":[],"updatedAt":"2025-03-25T08:38:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-25 08:38:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4870517","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4870517","identity":"rs-4870517","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}