Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 17, issue 1
Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-229-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-17-229-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 17, issue 1
Model experiment description paper
 | 
12 Jan 2024
Model experiment description paper |  | 12 Jan 2024

High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia

Pedro M. M. Soares, Frederico Johannsen, Daniela C. A. Lima, Gil Lemos, Virgílio A. Bento, and Angelina Bushenkova

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Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
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