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

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

doi:10.5194/gmd-18-9723-2025

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

Data sets

Precipitation observation and forecast in North China in 2022 by numerical model and deep learning model Fang Zuliang and Qi Zhong https://doi.org/10.57760/sciencedb.09821

Model code and software

Improving the fine structure of intense rainfall forecast by a designed adversarial generation network Fang Zuliang https://doi.org/10.5281/zenodo.14652556

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.