Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 23
Articles | Volume 18, issue 23
https://doi.org/10.5194/gmd-18-9723-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-9723-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 23
Development and technical paper
 | 
08 Dec 2025
Development and technical paper |  | 08 Dec 2025

Improving the fine structure of intense rainfall forecast by a designed generative adversarial network

Zuliang Fang, Qi Zhong, Haoming Chen, Xiuming Wang, Zhicha Zhang, and Hongli Liang

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Short summary
We developed a deep learning model based on Generative Adversarial Networks (GANs) to improve rainfall forecasts in northern China. Traditional models struggle with accuracy, especially for heavy rain. Our model merges data from multiple forecasts, capturing detailed rainfall patterns and offering more reliable short-term predictions.
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