Articles | Volume 15, issue 17
https://doi.org/10.5194/gmd-15-6747-2022
© Author(s) 2022. 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-15-6747-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model evaluation paper
| 
06 Sep 2022
Model evaluation paper |
| 06 Sep 2022
| 06 Sep 2022
Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
Rodrigo Manzanas
Departamento de Matemática Aplicada y Ciencias de la Computación (MACC), Universidad de Cantabria, Santander, Spain
Grupo de Meteorología y Computación, Universidad de Cantabria, Unidad Asociada al CSIC, Santander, Spain
Ezequiel Cimadevilla
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
Jesús Fernández
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
Jose González-Abad
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
Antonio S. Cofiño
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
José Manuel Gutiérrez
Instituto de Física de Cantabria (IFCA), CSIC–Universidad de Cantabria, Santander, Spain
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Short summary
Deep neural networks are used to produce downscaled regional climate change projections over Europe for temperature and precipitation for the first time. The resulting dataset, DeepESD, is analyzed against state-of-the-art downscaling methodologies, reproducing more accurately the observed climate in the historical period and showing plausible future climate change signals with low computational requirements.
Deep neural networks are used to produce downscaled regional climate change projections over...
