Future variation and uncertainty source decomposition in deep learning bias-corrected CMIP6 global extreme precipitation historical simulation

Global circulation models (GCMs) serve as pivotal tools in climate science research. Despite their critical role in understanding and predicting climate change, GCMs often exhibit significant discrepancies with observational data due to systematic and random errors, which has driven the progress of...

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主要な著者:	Xiaohua Xiang, Yongxuan Li, Xiaoling Wu, Zhu Liu, Lei Wu, Biqiong Wu, Chuanxin Jin, Zhiqiang Zeng
フォーマット:	Artigo
言語:	Inglês
出版事項:	Frontiers Media S.A. 2025-07-01
シリーズ:	Frontiers in Earth Science
主題:	extreme precipitation convolutional neural network CMIP6 uncertainty decomposition bias correction
オンライン･アクセス:	https://www.frontiersin.org/articles/10.3389/feart.2025.1601615/full
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