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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Detalhes bibliográficos

Principais autores:	Xiaohua Xiang, Yongxuan Li, Xiaoling Wu, Zhu Liu, Lei Wu, Biqiong Wu, Chuanxin Jin, Zhiqiang Zeng
Formato:	Artigo
Idioma:	Inglês
Publicado em:	Frontiers Media S.A. 2025-07-01
coleção:	Frontiers in Earth Science
Assuntos:	extreme precipitation convolutional neural network CMIP6 uncertainty decomposition bias correction
Acesso em linha:	https://www.frontiersin.org/articles/10.3389/feart.2025.1601615/full
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