Climate model downscaling in central Asia: a dynamical and a neural network approach

Fallah, Bijan; Rostami, Masoud; Russo, Emmanuele; Harder, Paula; Menz, Christoph; Hoffmann, Peter; Didovets, Iulii; Hattermann, Fred F.

doi:https://doi.org/10.5194/gmd-18-161-2025

Articles | Volume 18, issue 1

https://doi.org/10.5194/gmd-18-161-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-18-161-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 18, issue 1

Model experiment description paper

|

15 Jan 2025

Model experiment description paper |

| 15 Jan 2025

Climate model downscaling in central Asia: a dynamical and a neural network approach

Bijan Fallah, Masoud Rostami, Emmanuele Russo, Paula Harder, Christoph Menz, Peter Hoffmann, Iulii Didovets, and Fred F. Hattermann

Supplement

https://doi.org/10.5194/gmd-18-161-2025-supplement

Model code and software

Climate Model Downscaling in Central Asia: A Dynamical and a Neural Network Approach Bijan Fallah https://doi.org/10.5281/zenodo.10417111

Hard-Constrained Deep Learning for Climate Downscaling (v1.0.0) Paula Harder https://doi.org/10.5281/zenodo.8150694

Short summary

We tried to contribute to a local climate change impact study in central Asia, a region that is water-scarce and vulnerable to global climate change. We use regional models and machine learning to produce reliable local data from global climate models. We find that regional models show more realistic and detailed changes in heavy precipitation than global climate models. Our work can help assess the future risks of extreme events and plan adaptation strategies in central Asia.