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6902. model ensemble predictions of daily precipitation and temperature
Deng H, Hua W, Fan G, 2021. Evaluation and projection of near-sur‐ through machine learning techniques[J]. Scientific Reports, 12
face wind speed over China based on CMIP6 models[J]. Atmo‐ (1): 4678. DOI: 10. 1038/s41598-022-08786-w.
sphere, 12(8): 1062. DOI: 10. 3390/atmos12081062. Kew S F, Selten F M, Lenderink G, et al, 2013. The simultaneous
Dowdy A J, Catto J L, 2017. Extreme weather caused by concurrent occurrence of surge and discharge extremes for the Rhine delta
cyclone, front and thunderstorm occurrences[J]. Scientific Re‐ [J]. Natural Hazards and Earth System Sciences, 13(8): 2017-
ports, 7(1): 40359. DOI: 10. 1038/srep40359. 2029. DOI: 10. 5194/nhess-13-2017-2013.
Dueben P D, Bauer P, 2018. Challenges and design choices for global Kheir A M S, Elnashar A, Mosad A, et al, 2023. An improved deep
weather and climate models based on machine learning[J]. Geo‐ learning procedure for statistical downscaling of climate data[J].
scientific Model Development, 11(10): 3999-4009. DOI: 10. Heliyon, 9(7). DOI: 10. 1016/j. heliyon. 2023. e18200.
5194/gmd-11-3999-2018, 2018. Kim T, Yang T, Zhang L, et al, 2022. Near real-time hurricane rain‐
Eyring V, Bony S, Meehl G A, et al, 2016. Overview of the Coupled fall forecasting using convolutional neural network models with
Model Intercomparison Project Phase 6 (CMIP6) experimental Integrated Multi-satellitE Retrievals for GPM (IMERG) product
design and organization[J]. Geoscientific Model Development, 9 [J]. Atmospheric Research, 270: 106037. DOI: 10. 1016/j. at‐
(5), 1937-1958. DOI: 10. 5194/gmd-9-1937-2016. mosres. 2022. 106037.
Frank D, Reichstein M, Bahn M, et al, 2015. Effects of climate ex‐ Klutse N A B, Abiodun B J, Quagraine K A, et al, 2024. Projected
tremes on the terrestrial carbon cycle: concepts, processes and changes in rainfall extremes over West African cities under specif‐
potential future impacts[J]. Global change biology, 21(8): ic global warming levels using CORDEX and NEX-GDDP datas‐
2861-2880. DOI: 10. 1111/gcb. 12916. ets[J]. Earth Systems and Environment: 1-18. DOI: 10. 1007/
Feng J, Li D, Li Y, et al, 2023. Analysis of compound floods from s41748-024-00425-w.
storm surge and extreme precipitation in China[J]. Journal of Hy‐ Lai Y, Li J, Gu X, et al, 2021. Global compound floods from precipi‐
drology, 627: 130402. DOI: 10. 1016/j. jhydrol. 2023. 130402. tation and storm surge: Hazards and the roles of cyclones[J].
Ge F, Zhu S, Luo H, et al, 2021. Future changes in precipitation ex‐ Journal of Climate, 34(20): 8319-8339. DOI: 10. 1175/JCLI-
tremes over Southeast Asia: insights from CMIP6 multi-model D-21-0050. 1.
ensemble[J]. Environmental Research Letters, 16(2): 024013. Leonard M, Westra S, Phatak A, et al, 2014. A compound event
DOI: 10. 1088/1748-9326/abd7ad. framework for understanding extreme impacts[J]. Wiley Interdis‐
Giorgi F, Bi X, Pal J, 2004. Mean, interannual variability and trends ciplinary Reviews: Climate Change, 5(1): 113-128. DOI: 10.
in a regional climate change experiment over Europe. II: climate 1002/wcc. 252.
change scenarios (2071-2100)[J]. Climate Dynamics, 23: 839- Lyu Y, Zhu S, Zhi X, et al, 2023. Improving subseasonal‐to‐seasonal
858. DOI: 10. 1007/s00382-004-0467-0. prediction of summer extreme precipitation over southern China
Ham Y G, Kim J H, Luo J J, 2019. Deep learning for multi-year EN‐ based on a deep learning method[J]. Geophysical Research Let‐
SO forecasts[J]. Nature, 573(7775): 568-572. DOI: 10. 1038/ ters, 50(24): e2023GL106245. DOI: 10. 1029/2023GL106245.
s41586-019-1559-7. Martius O, Pfahl S, Chevalier C, 2016. A global quantification of com‐
Hao Z, Hao F, Xia Y, et al, 2022. Compound droughts and hot ex‐ pound precipitation and wind extremes[J]. Geophysical Research
tremes: Characteristics, drivers, changes, and impacts[J]. Letters, 43(14): 7709-7717. DOI: 10. 1002/2016GL070017.
Earth-Science Reviews, 235: 104241. DOI: 10. 1016/j. earsci‐ McPhillips L E, Chang H, Chester M V, et al, 2018. Defining ex‐
rev. 2022. 104241. treme events: A cross‐disciplinary review[J]. Earth's Future, 6
Hersbach H, Bell B, Berrisford P, et al, 2020. The ERA5 global re‐ (3): 441-455. DOI: 10. 1002/2017EF000686.
analysis[J]. Quarterly Journal of the Royal Meteorological Soci‐ Meng Y, Hao Z, Zhang Y, et al, 2023. Projection of compound wind
ety, 146(730): 1999-2049. DOI: 10. 1002/qj. 3803. and precipitation extremes in China based on Phase 6 of the Cou‐
Huang B, Liu Z, Duan Q, et al, 2024. Unsupervised deep learning bi‐ pled Model Intercomparison Project models[J]. International
as correction of CMIP6 global ensemble precipitation predictions Journal of Climatology, 43(3): 1396-1406. DOI: doi. org/10.
with cycle generative adversarial network[J]. Environmental Re‐ 1002/joc. 7922.
search Letters, 19(9): 094003. DOI: 10. 1088/1748-9326/ Qi C R, Su H, Nießner M, et al, 2016. Volumetric and multi-view
ad66e6. CNNs for object classification on 3D data[J]. IEEE Conference
Hu Y F, Yin F K, Zhang W M, 2021. Deep learning‐based precipita‐ on Computer Vision and Pattern Recognition (CVPR), 5648-
tion bias correction approach for Yin-He global spectral model 5656. DOI: 10. 1109/CVPR. 2016. 609.
[J]. Meteorological Applications, 28(5): e2032. DOI: 10. 1002/ Raveh‐Rubin S, Wernli H, 2015. Large‐scale wind and precipitation
met. 2032. extremes in the Mediterranean: a climatological analysis for
Jose D M, Vincent A M, Dwarakish G S, 2022. Improving multiple 1979-2012[J]. Quarterly Journal of the Royal Meteorological So‐

