Articles | Volume 19, issue 12
https://doi.org/10.5194/gmd-19-5781-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-5781-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
01 Jul 2026
Development and technical paper |
| 01 Jul 2026
| 01 Jul 2026
Pre-training for deep statistical climate downscaling: enhancing consistency and robustness across regional datasets
Jose González-Abad
CORRESPONDING AUTHOR
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
Maialen Iturbide
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
Alfonso Hernanz
Spanish Meteorological Agency (AEMET), Madrid, Spain
José Manuel Gutiérrez
Instituto de Física de Cantabria (IFCA), CSIC-Universidad de Cantabria, Santander, Spain
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Short summary
We explore how deep learning can improve local climate projections by adapting a national model to regional data. By relying on a paradigm called pre-training, we show that models can produce more consistent and physically aligned results, even when data is limited. This helps make future climate projections more reliable and supports better planning at both national and local levels.
We explore how deep learning can improve local climate projections by adapting a national model...
