ORIGINAL RESEARCH article
Front. Earth Sci.
Sec. Hydrosphere
Volume 13 - 2025 | doi: 10.3389/feart.2025.1601615
Future variation and uncertainty sources decomposition in the deep learning bias corrected CMIP6 global extreme precipitation historical simulation
Provisionally accepted
- 1Hohai University, Nanjing, China
- 2Jiangsu Water Conservancy Engineering Technology Consulting Co., Ltd, Nanjing, China
- 3China Yangtze Power Co. Ltd., Yichang, China
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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 Bias Correction (BC) techniques.This study explores the bias correction approach based on Convolutional Neural Networks (CNNs) to improve the accuracy of Expert Team on Climate Change variability. This research not only highlights the potential of CNN deep learning technique in enhancing the accuracy and reliability of extreme precipitation predictions, but also shows insights into uncertainty decomposition and variation to better understand various sources of uncertainty within climate projections.
Keywords: extreme precipitation, Convolutional Neural Network, CMIP6, Uncertainty decomposition, Bias Correction
Received: 28 Mar 2025; Accepted: 11 Jun 2025.
Copyright: © 2025 Xiang, Li, Wu, Liu, Wu, Wu, Jin and Zeng. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Zhu Liu, Hohai University, Nanjing, China
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