Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 19, issue 5
Articles | Volume 19, issue 5
https://doi.org/10.5194/gmd-19-1917-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-19-1917-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 5
Model description paper
 | 
06 Mar 2026
Model description paper |  | 06 Mar 2026

Assessing seasonal climate predictability using a deep learning application: NN4CAST

Víctor Galván Fraile, Belén Rodríguez-Fonseca, Irene Polo, Marta Martín-Rey, and María N. Moreno-García

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Latest update: 31 Oct 2026
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Short summary
We present a new deep learning framework designed to assess seasonal climate predictability by identifying the key predictors that influence climate variability across different regions. This tool enhances understanding of how remote areas are connected through climate interactions and providing accurate and explainable seasonal predictions. Our results demonstrate its potential to support more reliable and informed climate services at both regional and global scales.
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