Articles | Volume 28, issue 8
https://doi.org/10.5194/hess-28-1873-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-28-1873-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Apr 2024
Research article |
| 26 Apr 2024
| 26 Apr 2024
Variation and attribution of probable maximum precipitation of China using a high-resolution dataset in a changing climate
Jinghua Xiong
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
Abhishek
Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee 247667, India
Jiabo Yin
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
Chongyu Xu
Department of Geosciences, University of Oslo, P.O. Box 1047 Blindern, 0316, Oslo, Norway
Jun Wang
State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China
Jing Guo
Power China Huadong Engineering Corporation Limited, Hangzhou 311122, China
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Jinghua Xiong, Shenglian Guo, Jie Chen, and Jiabo Yin
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-645, https://doi.org/10.5194/hess-2021-645, 2022
Manuscript not accepted for further review
Short summary
Short summary
Although the “dry gets drier and wet gets wetter” (DDWW) paradigm is widely used to describe the trends in wetting and drying globally, we show that 27.1 % of global land agrees with the paradigm, while 22.4 % shows the opposite pattern during the period 1985–2014 from the perspective of terrestrial water storage change. Similar percentages are discovered under different scenarios during the future period. Our findings will benefit the understanding of hydrological responses under climate change.
Pengxiang Wang, Zuhao Zhou, Jiajia Liu, Chongyu Xu, Kang Wang, Yangli Liu, Jia Li, Yuqing Li, Yangwen Jia, and Hao Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2021-538, https://doi.org/10.5194/hess-2021-538, 2021
Manuscript not accepted for further review
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Hydrol. Earth Syst. Sci., 25, 5259–5275, https://doi.org/10.5194/hess-25-5259-2021, https://doi.org/10.5194/hess-25-5259-2021, 2021
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Ren Wang, Pierre Gentine, Jiabo Yin, Lijuan Chen, Jianyao Chen, and Longhui Li
Hydrol. Earth Syst. Sci., 25, 3805–3818, https://doi.org/10.5194/hess-25-3805-2021, https://doi.org/10.5194/hess-25-3805-2021, 2021
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Assessment of changes in the global water cycle has been a challenge. This study estimated long-term global latent heat and sensible heat fluxes for recent decades using machine learning and ground observations. The results found that the decline in evaporative fraction was typically accompanied by an increase in long-term runoff in over 27.06 % of the global land areas. The observation-driven findings emphasized that surface vegetation has great impacts in regulating water and energy cycles.
Tian Lan, Kairong Lin, Chong-Yu Xu, Zhiyong Liu, and Huayang Cai
Hydrol. Earth Syst. Sci., 24, 5859–5874, https://doi.org/10.5194/hess-24-5859-2020, https://doi.org/10.5194/hess-24-5859-2020, 2020
Zhengke Pan, Pan Liu, Chong-Yu Xu, Lei Cheng, Jing Tian, Shujie Cheng, and Kang Xie
Hydrol. Earth Syst. Sci., 24, 4369–4387, https://doi.org/10.5194/hess-24-4369-2020, https://doi.org/10.5194/hess-24-4369-2020, 2020
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This study aims to identify the response of catchment water storage capacity (CWSC) to meteorological drought by examining the changes of hydrological-model parameters after drought events. This study improves our understanding of possible changes in the CWSC induced by a prolonged meteorological drought, which will help improve our ability to simulate the hydrological system under climate change.
Cited articles
Afrooz, A. H., Akbari, H., Rakhshandehroo, G. R., and Pourtouiserkani, A.: Climate change impact on probable maximum precipitation in Chenar-Rahdar River basin, Water Manag., 36–47, https://doi.org/10.1061/9780784479322.004, 2015.
Afzali-Gorouh, Z., Faridhosseini, A., Bakhtiari, B., Mosaedi, A., and Salehnia, N.: Monitoring and projection of climate change impact on 24 h probable maximum precipitation in the Southeast of Caspian Sea, Nat. Hazards, 114, 77–99, 2022.
Beauchamp, J., Leconte, R., Trudel, M., and Brissette, F.: Estimation of the summer-fall PMP and PMF of a northern watershed under a changed climate, Water Resour. Res., 49, 3852–3862, 2011.
Berg, A. M., Lintner, B. R., Findell, K. L., Malyshev, S., Loikith, P. C., and Gentine, P.: Impact of soil moisture-atmosphere interactions on surface temperature distribution, J. Climate, 27, 7976–7993, 2014.
Casas, M. C., Rodríguez, R., Nieto, R., and Redano, A.: The estimation of probable maximum precipitation: the case of Catalonia, Ann. NY Acad. Sci., 1146, 291–302, 2008.
Chow, V. T.: A general formula for hydrologic frequency analysis, Eos T. Am. Geophys. Un., 32, 231–237, https://doi.org/10.1029/TR032i002p00231, 1951.
de Winter, R. C., Sterl, A., and Ruessink, B. G.: Wind extremes in the North Sea basin under climate change: An ensemble study of 12 CMIP5 GCMs, J. Geophys. Res.-Atmos., 118, 1601–1612, 2013.
Devitt, L., Neal, J., Coxon, G., Savage, J., and Wagener, T.: Flood hazard potential reveals global floodplain settlement patterns, Nat. Commun., 14, 2801, https://doi.org/10.1038/s41467-023-38297-9, 2023.
Diro, G. T., Sushama, L., Martynov, A., Jeong, D. I., Verseghy, D., and Winger, K.: Land-atmosphere coupling over North America in CRCM5, J. Geophys. Res.-Atmos., 119, 11955–11972, https://doi.org/10.1002/2014JD021677, 2014.
Donat, M. G., Lowry, A. L., Alexander, L. V., O'Gorman, P. A., and Maher, N.: More extreme precipitation in the world's dry and wet regions, Nat. Clim. Change, 6, 508–513, 2016.
Dong, T. and Dong, W.: Evaluation of Extreme Precipitation over Asia in CMIP6 Models, Clim. Dynam., 57, 1751–1769, https://doi.org/10.1007/s00382-021-05773-1, 2021.
Ekpetere, K. J.: Possibilities and limitations of IMERG Datasets for estimating probable maximum precipitation, University of Kansas, https://www.proquest.com/docview/2598679626?pq-origsite=gscholar&fromopenview=true (last access: 17 April 2024), 2021.
Ekpetere, K., Coll, J., Li, X., Kastens, J., and Mechem, D. B.: Global Probable Maximum Precipitation (PMP) datasets, HydroShare [data set], http://www.hydroshare.org/resource/9bed05f68ad444e8ad371d9db001007a (last access: 17 April 2024), 2023.
Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E.: Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geosci. Model Dev., 9, 1937–1958, https://doi.org/10.5194/gmd-9-1937-2016, 2016.
General Institute of Water Conservancy and Hydropower Planning and Design (GIWCHPD): Ministry of Water Resources Compilation of hydrological calculations of national large and medium-sized water conservancy and hydropower projects, Beijing, 1982.
General Institute of Water Conservancy and Hydropower Planning and Design (GIWCHPD): Ministry of Water Resources Compilation of hydrological calculations of national large and medium-sized water conservancy and hydropower projects, Beijing, 1990.
General Institute of Water Resources and Hydropower Planning and Design (GIWRHPD): Ministry of Water Resources, and Water Resources Protection Bureau of the Yangtze River Basin: Regulations for the compilation of water resources protection planning, SL 613-2013, China Water Resources and Hydropower Press, Beijing, 2013.
Gimeno, L., Vazquez, M., Eiras-Barca, J., Sorí, R., Algarra, I., and Nieto, R.: Atmospheric moisture transport and the decline in Arctic Sea ice, WIREs Clim. Change, 10, e588, https://doi.org/10.1002/wcc.588, 2019.
Gu, X., Ye, L., Xin, Q., Zhang, C., Zeng, F., Nerantzaki, S. D., and Papalexiou, S. M.: Extreme precipitation in China: a review, Adv. Water Resour., 163, 104144, https://doi.org/10.1016/j.advwatres.2022.104144, 2022.
Guerreiro, S. B., Fowler, H. J., Barbero, R., Westra, S., Lenderink, G., Blenkinsop, S., Lewis, E., and Li, X.-F.: Detection of continental-scale intensification of hourly rainfall extremes, Nat. Clim. Change, 8, 803–807, https://doi.org/10.1038/s41558-018-0245-3, 2018.
Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016.
Han, L., Wang, L., Chen, H., Xu, Y., Sun, F., Reed, K., Deng, X., and Li, W.: Impacts of long-term urbanization on summer rainfall climatology in Yangtze River Delta agglomeration of China, Geophys. Res. Lett., 49, e2021GL097546, https://doi.org/10.1029/2021GL097546, 2022.
Hansen, E. M.: Probable maximum precipitation for design floods in the United States, J. Hydrol., 96, 267–278, 1987.
He, J., Yang, K., Tang, W., Lu, H., Qin, J., Chen, Y., and Li, X.: The first high-resolution meteorological forcing dataset for land process studies over China, Sci. Data, 7, 25, https://doi.org/10.1038/s41597-020-0369-y, 2020.
Herbst, L. and Lalk, J.: A case study of climate variability effects on wind resources in South Africa, J. Energy South Afr, 25, 2–10, 2014.
Hershfield, D. M.: Estimating the probable maximum precipitation, J. Hydr. Eng. Div.-ASCE, 87, 99–116, 1961.
Hershfield, D. M.: Method for estimating probable maximum rainfall, J. Am. Water Works Ass., 57, 965–972, https://doi.org/10.1002/j.1551-8833.1965.tb01486.x, 1965.
Hirabayashi, Y., Mahendran, R., Koirala, S., Konoshima, L., Yamazaki, D., Watanabe, S., Kim, H., and Kanae, S.: Global flood risk under climate change, Nat. Clim. Change, 3, 816–821, https://doi.org/10.1038/nclimate1911, 2013.
Hiraga, Y., Iseri, Y., Warner, M. D., Frans, C. D., Duren, A. M., England, J. F., and Kavvas, M. L.: Estimation of long-duration maximum precipitation during a winter season for large basins dominated by atmospheric rivers using a numerical weather model, J. Hydrol., 598, 126224, https://doi.org/10.1016/j.jhydrol.2021.126224, 2021.
Huang, X. Y., and Stevenson, S.: Contributions of climate change and ENSO variability to future precipitation extremes over California, Geophys. Res. Lett., 50, e2023GL103322, https://doi.org/10.1029/2023GL103322, 2023.
Jakob, D., Smalley, R., Meighen, J., Xuereb, K. C., and Taylor, B. F.: Climate change and probable maximum precipitation, HRS Report no. 12, Hydrology Report Series, Australian Bureau of Meteorology, Melbourne, Australia, 2009.
Jia, Q., Jia, H., Li, Y., and Yin, D.: Applicability of CMIP5 and CMIP6 models in China: reproducibility of historical simulation and uncertainty of future projection, J. Climate, 36, 5809–5824, https://doi.org/10.1175/JCLI-D-22-0375.1, 2023.
Kendon, E. J., Ban, N., Roberts, N. M., Fowler, H. J., Roberts, M. J., Chan, S. C., Evans, J. P., Fosser, G., and Wilkinson, J. M.: Do convection-permitting regional climate models improve projections of future precipitation change?, B. Am. Meteorol. Soc., 98, 79–93, https://doi.org/10.1175/BAMS-D-15-0004.1, 2017.
Kenyon, J. and Hegerl, G. C.: Influence of modes of climate variability on global precipitation extremes, J. Climate, 23, 6248–6262, 2010.
Kim, S., Sharma, A., Wasko, C., and Nathan, R.: Linking total precipitable water to precipitation extremes globally, Earths Future, 10, e2021EF002473, https://doi.org/10.1029/2021EF002473, 2022.
Koster, R. D., Dirmeyer, P. A., Guo, Z., Bonan, G., Chan, E., Cox, P., Gordon, C. T., Kanae, S., Kowalczyk, E., Lawrence, D., Liu, P., Lu, C. H., Malyshev, S., McAvaney, B., Mitchell, K., Mocko, D., Oki, T., Oleson, K., Pitman, A., Sud, Y. C., Taylor, C. M., Verseghy, D., Vasic, R., Xue, Y. K., and Yamada, T.: Regions of strong coupling between soil moisture and precipitation, Science, 305, 1138–1140, 2004.
Kunkel, K. E., Karl, T. R., Easterling, D. R., Redmond, K., Young, J., Yin, X. G., and Hennon, P.: Probable maximum precipitation and climate change, Geophys Res. Lett., 40, 1402–1408, 2013.
Lan, P., Lin, B., Zhang, Y., and Chen, H.: Probable maximum precipitation estimation using the revised value method in Hong Kong, J. Hydrol. Eng., 22, 05017008, https://doi.org/10.1061/(ASCE)HE.1943-5584.0001517, 2017.
Lee, O. and Kim, S.: Future PMPs projection under future dew point temperature variation of RCP 8.5 climate change scenario, J. Korean Soc. Hazard Mitig., 16, 505–514, 2016.
Lehner, B., Liermann, C. R., Revenga, C., Vörömsmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., Nilsson, C., Robertson, J. C., Rödel, R., Sindorf, N., and Wisser, D.: High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management, Front. Ecol. Environ., 9, 494–502, https://doi.org/10.1890/100125, 2011a.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete, B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J., and Nilsson, C.: High-resolution mapping of the world’s reservoirs and dams for sustainable river-flow management, Global Dam Watch [data set], https://ln.sync.com/dl/bd47eb6b0/anhxaikr-62pmrgtq-k44xf84f-pyz4atkm/view/default/447819520013, (last access: 18 April 2024), 2011b.
Liao, Y., Lin, B., Chen, X., and Ding, H.: A new look at storm separation technique in estimation of probable maximum precipitation in mountainous areas, Water, 12, 1177, https://doi.org/10.3390/w12041177, 2020.
Lin, B. Z.: Application of statistical estimation in study of probable maximum precipitation, Journal of Hohai University (Natural Sciences), 1, 52–63, 1981.
Liu, B. J., Tan, X. Z., Gan, T. Y., Chen, X. H., Liu, K. R., Lu, M. Q., and Liu, Z. Y.: Global atmospheric moisture transport associated with precipitation extremes: Mechanisms and climate change impacts, WIREs Water, 7, e1412, https://doi.org/10.1002/wat2.1412, 2020.
Liu, T., Liang, Z., Chen, Y., Lei, X., and Li, B.: Long-duration PMP and PMF estimation with SWAT model for the sparsely gauged Upper Nujiang River Basin, Nat. Hazards, 90, 735–755, 2018.
Lorenz, R., Argüeso, D., Donat, M. G., Pitman, A. J., van den Hurk, B., Berg, A., Lawrence, D. M., Chéruy, F., Ducharne, A., Hagemann, S., Meier, A., Milly, P. C. D., and Seneviratne, S. I.: Influence of land-atmosphere feedbacks on temperature and precipitation extremes in the GLACE-CMIP5 ensemble, J. Geophys. Res.-Atmos., 121, 607–623, 2016.
Loriaux, J. M., Lenderink, G., and Siebesma, A. P.: Large-scale controls on extreme precipitation, J. Climate, 30, 955–968, 2016.
Luo, P. P., Mu, D. R., Xue, H., Ngo-Duc, T., Dang-Dinh, K., Takara, K., Nover, D., and Schladow, G.: Flood inundation assessment for the Hanoi Central Area, Vietnam under historical and extreme rainfall conditions, Sci. Rep., 8, 12623, https://doi.org/10.1038/s41598-018-30024-5, 2018.
Mann, H. B.: Nonparametric tests against trend, Econometrica, 13, 245–259, 1945.
Martinez-Villalobos, C. and Neelin, J. D.: Shifts in precipitation accumulation extremes during the warm season over the United States, Geophys. Res. Lett., 45, 8586–8595, https://doi.org/10.1029/2018GL078465, 2018.
Monjo, R., Locatelli, L., Milligan, J., Torres, L., Velasco, M., Gaitán, M., Pórtoles, J, Redolat, D., Russo, B., and Ribalaygua, J.: Estimation of future extreme rainfall in Barcelona (Spain) under monofractal hypothesis, Int. J. Climatol., 43, 4047–4068, 2023.
Mudd, L., Wang, Y., Letchford, C., and Rosowsky, D.: Assessing climate change impact on the US east coast hurricane hazard: temperature, frequency, and track, Nat. Hazards Rev., 15, 04014001, https://doi.org/10.1061/(ASCE)NH.1527-6996.0000128, 2014.
O'Neill, B. C., Tebaldi, C., van Vuuren, D. P., Eyring, V., Friedlingstein, P., Hurtt, G., Knutti, R., Kriegler, E., Lamarque, J.-F., Lowe, J., Meehl, G. A., Moss, R., Riahi, K., and Sanderson, B. M.: The Scenario Model Intercomparison Project (ScenarioMIP) for CMIP6, Geosci. Model Dev., 9, 3461–3482, https://doi.org/10.5194/gmd-9-3461-2016, 2016.
Papalexiou, S. M. and Koutsoyiannis, D.: A probabilistic approach to the concept of Probable Maximum Precipitation, Adv. Geosci., 7, 51–54, https://doi.org/10.5194/adgeo-7-51-2006, 2006.
Papalexiou, S. M., Dialynas, Y. G., and Grimaldi, S.: Hershfield factor revisited: correcting annual maximum precipitation, J. Hydrol., 542, 884–895, https://doi.org/10.1016/j.jhydrol.2016.09.058, 2016.
Park, M., Park, M. J., Kim, S., and Joo, J.: Extreme storm estimation by climate change using precipitable water, J. Korean Soc. Hazard Mitig., 13, 121–127, 2013.
Piao, S. L., Ciais, P., Huang, Y., Shen, Z. B., Peng, S. B., Li, J. B., Zhou, L. P., Liu, H. Y., Ma, Y. C., Ding, Y. H., Friedlingstein, P., Liu, C. Z., Tan, K., Yu, Y. Q., Zhang, T. Y., and Fang, J. Y.: The impacts of climate change on water resources and agriculture in China, Nature, 467, 43–51, https://doi.org/10.1038/nature09364, 2010.
Qiao, L., Zuo, Z., Zhang, R., Piao, S. L., Xiao, D., and Zhang, K. W.: Soil moisture–atmosphere coupling accelerates global warming, Nat. Commun., 14, 4908, https://doi.org/10.1038/s41467-023-40641-y, 2023.
Qin, R. and Zhang, F.: HRLT: A high-resolution (1 day, 1 km) and long-term (1961–2019) gridded dataset for temperature and precipitation across China [dataset], PANGAEA [data set], https://doi.org/10.1594/PANGAEA.941329, 2022.
Qin, R., Zhao, Z., Xu, J., Ye, J.-S., Li, F.-M., and Zhang, F.: HRLT: a high-resolution (1 d, 1 km) and long-term (1961–2019) gridded dataset for surface temperature and precipitation across China, Earth Syst. Sci. Data, 14, 4793–4810, https://doi.org/10.5194/essd-14-4793-2022, 2022.
Rajulapati, C. R., Papalexiou, S. M., Clark, M. P., Razavi, S., Tang, G., and Pomeroy, J. W.: Assessment of extremes in global precipitation products: How reliable are they?, J. Hydrometeorol., 21, 2855–2873, https://doi.org/10.1175/JHM-D-20-0040.1, 2020.
Rajulapati, C. R., Papalexiou, S. M., Clark, M. P., and Pomeroy, J. W.: The Perils of Regridding: Examples Using a Global Precipitation Dataset, J. Appl. Meteorol. Clim., 60, 1561–1573, https://doi.org/10.1175/JAMC-D-20-0259.1, 2021.
Rastogi, D., Kao, S. C., Ashfaq, M., Mei, R., Kabela, E. D., Gangrade, S., Naz, N. S., Preston, B. L., Singh, N., and Anantharaj, V. G.: Effects of climate change on probable maximum precipitation: A sensitivity study over the Alabama-Coosa-Tallapoosa River basin, J. Geophys. Res.-Atmos., 122, 4808–4828, https://doi.org/10.1002/2016jd026001, 2017.
Richter, I. and Xie, S. P.: Moisture transport from the Atlantic to the Pacific basin and its response to North Atlantic cooling and global warming, Clim. Dynam., 35, 551–566, 2010.
Rouhani, H. and Leconte, R.: A novel method to estimate the maximization ratio of the probable maximum precipitation (PMP) using regional climate model output, Water Resour. Res., 52, 7347–7365, 2016.
Rousseau, A. N., Klein, I. M., Freudiger, D., Gagnon, P., Frigon, A., and Ratte-Fortin, C.: Development of a methodology to evaluate probable maximum precipitation (PMP) under changing climate conditions: Application to southern Quebec, Canada, J. Hydrol., 519, 3094–3109, 2014.
Salas, J. D. and Obeysekera, J.: Revisiting the concepts of return period and risk under non-stationary conditions, J. Hydrol. Eng., 19, 554–568, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000820, 2014.
Salas, J. D., Anderson, M. L., Papalexiou, S. M., and Frances, F.: PMP and climate variability and change: A review, J. Hydrol. Eng., 25, 03120002, https://doi.org/10.1061/(ASCE)HE.1943-5584.0002003, 2020.
Sarkar, S. and Maity, R.: Increase in probable maximum precipitation in a changing climate over India, J. Hydrol., 585, 124806, https://doi.org/10.1016/j.jhydrol.2020.124806, 2020.
Sarkar, S. and Maity, R.: Global climate shift in 1970s causes a significant worldwide increase in precipitation extremes, Sci. Rep., 11, 11574, https://doi.org/10.1038/s41598-021-90854-8, 2021.
Seneviratne, S. I., Corti, T., Davin, E. L., Hirschi, M., Jaeger, E. B., Lehner, I., Orlowsky, B., and Teuling, A. J.: Investigating soil moisture–climate interactions in a changing climate: a review, Earth-Sci. Rev., 99, 125–161, 2010.
Shi, C., Jiang, L., Zhang, T., Xu, B., and Han, S.: Status and plans of CMA Land Data Assimilation System (CLDAS) Project, Geophys. Res. Abstr., EGU2014-5671, EGU General Assembly 2014, Vienna, Austria, 2014.
Song, C., Fan, C., Zhu, J., Wang, J., Sheng, Y., Liu, K., Chen, T., Zhan, P., Luo, S., Yuan, C., and Ke, L.: A comprehensive geospatial database of nearly 100 000 reservoirs in China, Earth Syst. Sci. Data, 14, 4017–4034, https://doi.org/10.5194/essd-14-4017-2022, 2022.
Sun, Q., Miao, C., and Duan, Q.: Changes in the spatial heterogeneity and annual distribution of observed precipitation across China, J. Climate, 30, 9399–9416, 2017.
Svensson, C. and Rakhecha, P. R.: Estimation of probable maximum precipitation for dams in the Hongru river catchment, China, Theor. Appl. Climatol., 59, 79–91, 1998.
Tang, G., Clark, M. P., Papalexiou, S. M., Ma, Z., and Hong, Y.: Have satellite precipitation products improved over last two decades? A comprehensive comparison of GPM IMERG with nine satellite and reanalysis datasets, Remote Sens. Environ., 240, 111697, https://doi.org/10.1016/j.rse.2020.111697, 2020.
Tang, G. A.: Digital elevation model of China (1KM), A Big Earth Data Platform for Three Poles, https://data.tpdc.ac.cn/en/data/12e91073-0181-44bf-8308-c50e5bd9a734/ (last access: 18 April 2024), 2019.
Trenberth, K. E., Dai, A., Rasmussen, R. M., and Parsons, D. B.: The changing character of precipitation, B. Am. Meteorol. Soc., 84, 1205–1217, 2003.
van den Hurk, B., Kim, H., Krinner, G., Seneviratne, S. I., Derksen, C., Oki, T., Douville, H., Colin, J., Ducharne, A., Cheruy, F., Viovy, N., Puma, M. J., Wada, Y., Li, W., Jia, B., Alessandri, A., Lawrence, D. M., Weedon, G. P., Ellis, R., Hagemann, S., Mao, J., Flanner, M. G., Zampieri, M., Materia, S., Law, R. M., and Sheffield, J.: LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project – aims, setup and expected outcome, Geosci. Model Dev., 9, 2809–2832, https://doi.org/10.5194/gmd-9-2809-2016, 2016.
van Dilke, A. J. H., Herold, M., Mallick, K., Benedict, I., Machwitz, M., Schlerf, M., Pranindita, A., Theeuwen, J. J. E., Bastin, J. F., and Teuling, A. J.: Shifts in regional water availability due to global tree restoration, Nat. Geosci., 15, 363–368, 2022.
Visser, J. B., Kim, S., Wasko, C., Nathan, R., and Sharma, A.: The impact of climate change on operational probable maximum precipitation estimates, Water Resour. Res., 58, e2022WR032247, https://doi.org/10.1029/2022WR032247, 2022.
Wang, G. A.: Principles and methods of PMP/PMF calculations, China Water Power Press and Yellow River Water Resources Publishing House, Beijing, ISBN 7806213236, 1999.
Wang, J. Q.: Rainstorms in China, China Water & Power Press, Beijing, ISBN 9787508405605, 2002.
WCRP: CMIP6, WCRP [data set], https://esgf-data.dkrz.de/search/cmip6-dkrz/, last access: 18 April 2024.
Winsemius, H. C., Aerts, J. C. J. H., van Beek, L. P. H., Bierkens, M. F. P., Bouwman, A., Jongman, B., Kwadijk, J. C. J., Ligtvoet, W., Lucas, P. L., van Vuuren, D. P., and Ward, P. J.: Global drivers of future river flood risk, Nat. Clim. Change, 6, 381–385, https://doi.org/10.1038/nclimate2893, 2016.
Working Committee of Natural Regionalization, Chinese Academy of Sciences (WCNR): Climate regionalization in China, Science Press, Beijing, ISBN 12031⋅66, 1959.
World Meteorological Organization (WMO): Manual on estimation of probable maximum precipitation (PMP), WMO-No. 1045, 1–7, ISBN 978-926-3101045-9, 2009.
Wu, S. T., Wei, Z. G., Li, X. R., and Ma, L.: Land-atmosphere coupling effects of soil temperature and moisture on extreme precipitation in the arid regions of Northwest China, Front. Earth Sci., 10, 1079131, https://doi.org/10.3389/feart.2022.1079131, 2023.
Wu, W., Ren, L., Wei, Y., and Guo, M.: Impacts of urbanization on extreme regional precipitation events, Discrete Dyn. Nat. Soc., 17, 2210184, https://doi.org/10.1155/2021/2210184, 2021.
Xiong, J. H., Wang, Z. L., Lai, C. G., and Liao, Y. X.: Spatiotemporal variability of sunshine duration and influential climatic factors in mainland China during 1959–2017, Int. J. Climatol., 40, 6282–6300, 2020.
Yang, Y., Tang, G., Lei, X., Hong, Y., and Yang, N.: Can satellite precipitation products estimate probable maximum precipitation: a comparative investigation with gauge data in the Dadu River basin, Remote Sens.-Basel, 10, 41, https://doi.org/10.3390/rs10010041, 2018.
Yin, J. B., Guo, S. L., Gu, L., Yang, G., Wang, J., and Yang, Y.: Thermodynamic response of precipitation extremes to climate change and its impacts on floods over China, Chinese Sci. Bull., 66, 4315–4325, 2021 (in Chinese).
Yin, J. B., Guo, S. L., Wang, J., Chen, J., Zhang, Q., Gu, L., Yang, Y., Tian, J., Xiong, L. H., and Zhang, Y.: Thermodynamic driving mechanisms for the formation of global precipitation extremes and ecohydrological effects, Sci. China Earth Sci., 66, 92–110, 2023.
Zhao, W., Kinouchi, T., Ang, R., Zhuang, Q., and Zhuang, Q.: A framework for quantifying climate-informed heavy rainfall change: Implications for adaptation strategies, Sci. Total Environ., 835, https://doi.org/10.1016/j.scitotenv.2022.155553, 2022.
Zhao, W., Abhishek, Takhellambam, B. S., Zhang, J., Zhao, Y., and Kinouchi, T.: Spatiotemporal variability of current and future sub-daily rainfall in Japan using state-of-the-art high-quality data sets, Water Resour. Res., 59, e2022WR034305, https://doi.org/10.1029/2022WR034305, 2023.
Zhao, Y. and Zhu, J.: Accuracy and evaluation of precipitation grid daily data sets in China in recent 50 years, Plateau Meteorology, 34, 50–58, 2015.
Zheng, Y., Kumar, A., and Niyogi, D.: Impacts of land–atmosphere coupling on regional rainfall and convection, Clim. Dynam., 44, 2383–2409, 2015.
Zhou, S., Williams, A. P., Berg, A. M., Cook, B. I., Zhang, Y., Hagemann, S., Lorenz, R., Seneviratne, S., and Gentine, P.: Land–atmosphere feedbacks exacerbate concurrent soil drought and atmospheric aridity, P. Natl. Acad. Sci. USA, 116, 18848–18853, 2019.
Zhou, S., Williams, A. P., Lintner, B. R., Findell, K. L., Keenan, T. F., Zhang, Y., and Gentine, P.: Diminishing seasonality of subtropical water availability in a warmer world dominated by soil moisture–atmosphere feedbacks, Nat. Commun., 13, 5756, https://doi.org/10.1038/s41467-022-33473-9, 2022.
Zhou, Y., Liang, Z., Hu, Y., Li, D., Liu, T., and Lei, X.: An improved moisture and wind maximization method for probable maximum precipitation estimation and its application to a small catchment in China, Int. J. Climatol., 40, 2624–2638, 2020.
Zhu, H., Jiang, Z., Li, J., Li, W., Sun, C., and Li, L.: Does CMIP6 inspire more confidence in simulating climate extremes over China?, Adv. Atmos. Sci., 37, 1119–1132, https://doi.org/10.1007/s00376-020-9289-1, 2020.
Short summary
Temporal variability and spatial heterogeneity of climate systems challenge accurate estimation of probable maximum precipitation (PMP) in China. We use high-resolution precipitation data and climate models to explore the variability, trends, and shifts of PMP under climate change. Validated with multi-source estimations, our observations and simulations show significant spatiotemporal divergence of PMP over the country, which is projected to amplify in future due to land–atmosphere coupling.
Temporal variability and spatial heterogeneity of climate systems challenge accurate estimation...
