Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 4
Articles | Volume 19, issue 4
https://doi.org/10.5194/gmd-19-1791-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1791-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 4
Development and technical paper
 | 
03 Mar 2026
Development and technical paper |  | 03 Mar 2026

Conditional diffusion models for downscaling and bias correction of Earth system model precipitation

Michael Aich, Philipp Hess, Baoxiang Pan, Sebastian Bathiany, Yu Huang, and Niklas Boers

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Short summary
Accurately simulating rainfall is essential to understand the impacts of climate change, especially extreme events such as floods and droughts. Climate models simulate the atmosphere at a coarse resolution and often misrepresent precipitation, leading to biased and overly smooth fields. We improve the precipitation using a machine learning model that is data-efficient, preserves key climate signals such as trends and variability, and significantly improves the representation of extreme events.
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