Articles | Volume 15, issue 17
Geosci. Model Dev., 15, 6747–6758, 2022
https://doi.org/10.5194/gmd-15-6747-2022
Geosci. Model Dev., 15, 6747–6758, 2022
https://doi.org/10.5194/gmd-15-6747-2022
Model evaluation paper
06 Sep 2022
Model evaluation paper | 06 Sep 2022

Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44

Jorge Baño-Medina et al.

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Cited articles

Bandhauer, M., Isotta, F., Lakatos, M., Lussana, C., Båserud, L., Izsák, B., Szentes, O., Tveito, O. E., and Frei, C.: Evaluation of daily precipitation analyses in E-OBS (v19. 0e) and ERA5 by comparison to regional high-resolution datasets in European regions, Int. J. Climatol., 42, 727–747, 2022. a
Baño-Medina, J.: Understanding Deep Learning Decisions in Statistical Downscaling Models, in: Proceedings of the 10th International Conference on Climate Informatics, 79–85, 2020. a
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: Configuration and intercomparison of deep learning neural models for statistical downscaling, Geosci. Model Dev., 13, 2109–2124, https://doi.org/10.5194/gmd-13-2109-2020, 2020. a, b, c, d, e, f, g, h, i, j
Baño-Medina, J., Manzanas, R., and Gutiérrez, J. M.: On the suitability of deep convolutional neural networks for continental-wide downscaling of climate change projections, Clim. Dynam., 57, 1–11, 2021. a, b, c, d, e, f, g
Baño-Medina, J., Manzanas, R., Cimadevilla, E., Fernández, J., González-Abad, J., Cofiño, A. S., and Gutiérrez, J. M.: 2022_Bano_DeepESD_GMD_data (1.0.0), Zenodo [data set], https://doi.org/10.5281/zenodo.6823422, 2022a. a
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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.