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

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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Bibliographic Details
Main Authors: Xiaohua Xiang, Yongxuan Li, Xiaoling Wu, Zhu Liu, Lei Wu, Biqiong Wu, Chuanxin Jin, Zhiqiang Zeng
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-07-01
Series:Frontiers in Earth Science
Subjects:
extreme precipitation
convolutional neural network
CMIP6
uncertainty decomposition
bias correction
Online Access:https://www.frontiersin.org/articles/10.3389/feart.2025.1601615/full
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https://www.frontiersin.org/articles/10.3389/feart.2025.1601615/full

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