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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23 citations as recorded by crossref.
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- Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models S. Lin et al. 10.1038/s41597-025-04925-w
- Machine Learning Methods in Weather and Climate Applications: A Survey L. Chen et al. 10.3390/app132112019
- Deep‐learning‐based downscaling of precipitation in the middle reaches of the Yellow River using residual‐based CNNs H. Fu et al. 10.1002/qj.4759
- Exploring super-resolution spatial downscaling of several meteorological variables and potential applications for photovoltaic power A. Damiani et al. 10.1038/s41598-024-57759-8
- Diffusion model-based probabilistic downscaling for 180-year East Asian climate reconstruction F. Ling et al. 10.1038/s41612-024-00679-1
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- Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian 10.1007/s00382-024-07406-9
- High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia P. Soares et al. 10.5194/gmd-17-229-2024
- Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks T. Asfaw & J. Luo 10.1007/s00376-023-3029-2
- Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects S. Piccolroaz et al. 10.1029/2023RG000816
- Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review Y. Sun et al. 10.1016/j.isprsjprs.2023.12.011
- A systematic review of predictor screening methods for downscaling of numerical climate models A. Baghanam et al. 10.1016/j.earscirev.2024.104773
- Projecting future snow changes at kilometer scale for adaptation using machine learning and a CMIP6 multi-model ensemble A. Damiani et al. 10.1016/j.scitotenv.2025.178606
- Regional climate projections of daily extreme temperatures in Argentina applying statistical downscaling to CMIP5 and CMIP6 models R. Balmaceda-Huarte et al. 10.1007/s00382-024-07147-9
- Estimating pros and cons of statistical downscaling based on EQM bias adjustment as a complementary method to dynamical downscaling A. Reder et al. 10.1038/s41598-024-84527-5
- Using Machine Learning to Cut the Cost of Dynamical Downscaling S. Hobeichi et al. 10.1029/2022EF003291
- An improved deep learning procedure for statistical downscaling of climate data A. Kheir et al. 10.1016/j.heliyon.2023.e18200
- Deep Learning Downscaling of Precipitation Projection over Central Asia Y. Jiang et al. 10.3390/w17071089
- On the deep learning approach for improving the representation of urban climate: The Paris urban heat island and temperature extremes F. Johannsen et al. 10.1016/j.uclim.2024.102039
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- A dataset of 0.05-degree leaf area index in China during 1983–2100 based on deep learning network H. Li et al. 10.1038/s41597-024-03948-z
- Downscaling multi-model climate projection ensembles with deep learning (DeepESD): contribution to CORDEX EUR-44 J. Baño-Medina et al. 10.5194/gmd-15-6747-2022
22 citations as recorded by crossref.
- Comparison of a novel machine learning approach with dynamical downscaling for Australian precipitation N. Nishant et al. 10.1088/1748-9326/ace463
- Monthly 0.05° winter months snow depth dataset for the Northern Hemisphere from 21 CMIP6 models S. Lin et al. 10.1038/s41597-025-04925-w
- Machine Learning Methods in Weather and Climate Applications: A Survey L. Chen et al. 10.3390/app132112019
- Deep‐learning‐based downscaling of precipitation in the middle reaches of the Yellow River using residual‐based CNNs H. Fu et al. 10.1002/qj.4759
- Exploring super-resolution spatial downscaling of several meteorological variables and potential applications for photovoltaic power A. Damiani et al. 10.1038/s41598-024-57759-8
- Diffusion model-based probabilistic downscaling for 180-year East Asian climate reconstruction F. Ling et al. 10.1038/s41612-024-00679-1
- On the use of convolutional neural networks for downscaling daily temperatures over southern South America in a climate change scenario R. Balmaceda-Huarte et al. 10.1007/s00382-023-06912-6
- Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian 10.1007/s00382-024-07406-9
- High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia P. Soares et al. 10.5194/gmd-17-229-2024
- Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks T. Asfaw & J. Luo 10.1007/s00376-023-3029-2
- Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects S. Piccolroaz et al. 10.1029/2023RG000816
- Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review Y. Sun et al. 10.1016/j.isprsjprs.2023.12.011
- A systematic review of predictor screening methods for downscaling of numerical climate models A. Baghanam et al. 10.1016/j.earscirev.2024.104773
- Projecting future snow changes at kilometer scale for adaptation using machine learning and a CMIP6 multi-model ensemble A. Damiani et al. 10.1016/j.scitotenv.2025.178606
- Regional climate projections of daily extreme temperatures in Argentina applying statistical downscaling to CMIP5 and CMIP6 models R. Balmaceda-Huarte et al. 10.1007/s00382-024-07147-9
- Estimating pros and cons of statistical downscaling based on EQM bias adjustment as a complementary method to dynamical downscaling A. Reder et al. 10.1038/s41598-024-84527-5
- Using Machine Learning to Cut the Cost of Dynamical Downscaling S. Hobeichi et al. 10.1029/2022EF003291
- An improved deep learning procedure for statistical downscaling of climate data A. Kheir et al. 10.1016/j.heliyon.2023.e18200
- Deep Learning Downscaling of Precipitation Projection over Central Asia Y. Jiang et al. 10.3390/w17071089
- On the deep learning approach for improving the representation of urban climate: The Paris urban heat island and temperature extremes F. Johannsen et al. 10.1016/j.uclim.2024.102039
- Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain D. Quesada‐Chacón et al. 10.1029/2023EF003531
- A dataset of 0.05-degree leaf area index in China during 1983–2100 based on deep learning network H. Li et al. 10.1038/s41597-024-03948-z
Latest update: 17 May 2025
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...
