Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 1
Articles | Volume 19, issue 1
https://doi.org/10.5194/gmd-19-27-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-27-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 1
Development and technical paper
 | 
05 Jan 2026
Development and technical paper |  | 05 Jan 2026

Increasing resolution and accuracy in sub-seasonal forecasting through 3D U-Net: the western US

Jihun Ryu, Hisu Kim, Shih-Yu (Simon) Wang, and Jin-Ho Yoon

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Cited articles

Aich, M., Hess, P., Pan, B., Bathiany, S., Huang, Y., and Boers, N.: Conditional diffusion models for downscaling and bias correction of Earth system model precipitation, arXiv [preprint], https://doi.org/10.48550/arXiv.2404.14416, 2024. a
Ardilouze, C., Batté, L., and Déqué, M.: Subseasonal-to-seasonal (S2S) forecasts with CNRM-CM: a case study on the July 2015 West-European heat wave, Advances in Science and Research, 14, 115–121, 2017. a
Bi, K., Xie, L., Zhang, H., Chen, X., Gu, X., and Tian, Q.: Accurate medium-range global weather forecasting with 3D neural networks, Nature, 619, 533–538, 2023. a
Bonavita, M.: On some limitations of current machine learning weather prediction models, Geophys. Res. Lett., 51, e2023GL107377, https://doi.org/10.1029/2023GL107377, 2024. a
Chen, L., Zhong, X., Li, H., Wu, J., Lu, B., Chen, D., Xie, S.-P., Wu, L., Chao, Q., Lin, C., Hu Z., and Qi Y.: A machine learning model that outperforms conventional global subseasonal forecast models, Nat. Commun., 15, 6425, https://doi.org/10.1038/s41467-024-50714-1, 2024. a
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Using a neural network model, county-level weather forecasts was achieved in the Western US. By combining traditional forecasting data with actual weather observations, the AI system achieved better temperature predictions at local scales. While showed promise for temperature forecasting, it still had difficulty accurately predicting extreme rainfall events. The research advances weather forecasting capabilities, potentially helping communities prepare for severe weather conditions.
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