Articles | Volume 17, issue 1
https://doi.org/10.5194/gmd-17-229-2024
© Author(s) 2024. 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-17-229-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Model experiment description paper
| 
12 Jan 2024
Model experiment description paper |
| 12 Jan 2024
| 12 Jan 2024
High-resolution downscaling of CMIP6 Earth system and global climate models using deep learning for Iberia
Pedro M. M. Soares
Instituto Dom Luiz, IDL, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal
Frederico Johannsen
CORRESPONDING AUTHOR
Instituto Dom Luiz, IDL, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal
Daniela C. A. Lima
Instituto Dom Luiz, IDL, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal
Gil Lemos
Instituto Dom Luiz, IDL, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal
Virgílio A. Bento
Instituto Dom Luiz, IDL, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal
Angelina Bushenkova
Instituto Dom Luiz, IDL, Faculty of Sciences, University of Lisbon, 1749-016 Lisbon, Portugal
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Cited
15 citations as recorded by crossref.
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- Increased population exposure to extreme droughts in Iberia due to 0.5 °C additional anthropogenic warming A. Russo et al. 10.1088/1748-9326/ad975d
- 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
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- Recent heatwaves as a prelude to climate extremes in the western Mediterranean region E. Tejedor et al. 10.1038/s41612-024-00771-6
- Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy C. Jones et al. 10.5194/esd-15-1319-2024
- Machine Learning Methods for Weather Forecasting: A Survey H. Zhang et al. 10.3390/atmos16010082
- Storylines of projected summer warming in Iberia using atmospheric circulation, soil moisture and sea surface temperature as drivers of uncertainty J. Garrido-Perez et al. 10.1016/j.atmosres.2024.107677
- Dynamical-generative downscaling of climate model ensembles I. Lopez-Gomez et al. 10.1073/pnas.2420288122
- Towards an improved representation of the urban heat island effect : A multi-scale application of XGBoost for madrid A. Bushenkova et al. 10.1016/j.uclim.2024.101982
- 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
- Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin C. Thakur et al. 10.1016/j.ejrh.2025.102443
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14 citations as recorded by crossref.
- The outstanding European and Mediterranean heatwave activity during summer 2022 R. Trigo et al. 10.1016/j.atmosres.2025.108195
- Cluster-based downscaling of precipitation using Kolmogorov-Arnold Neural Networks and CMIP6 models: Insights from Oman A. Mardy et al. 10.1016/j.jenvman.2025.124971
- Increased population exposure to extreme droughts in Iberia due to 0.5 °C additional anthropogenic warming A. Russo et al. 10.1088/1748-9326/ad975d
- 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
- 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
- On the impact of climate change on urban microclimate, thermal comfort, and human health: Multiscale numerical simulations N. Antoniou et al. 10.1016/j.buildenv.2024.111690
- Recent heatwaves as a prelude to climate extremes in the western Mediterranean region E. Tejedor et al. 10.1038/s41612-024-00771-6
- Bringing it all together: science priorities for improved understanding of Earth system change and to support international climate policy C. Jones et al. 10.5194/esd-15-1319-2024
- Machine Learning Methods for Weather Forecasting: A Survey H. Zhang et al. 10.3390/atmos16010082
- Storylines of projected summer warming in Iberia using atmospheric circulation, soil moisture and sea surface temperature as drivers of uncertainty J. Garrido-Perez et al. 10.1016/j.atmosres.2024.107677
- Dynamical-generative downscaling of climate model ensembles I. Lopez-Gomez et al. 10.1073/pnas.2420288122
- Towards an improved representation of the urban heat island effect : A multi-scale application of XGBoost for madrid A. Bushenkova et al. 10.1016/j.uclim.2024.101982
- 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
- Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin C. Thakur et al. 10.1016/j.ejrh.2025.102443
1 citations as recorded by crossref.
Latest update: 09 May 2025
Short summary
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula. Four DL architectures were evaluated and trained using historical climate data and then used to downscale future projections from the global models. These show agreement with the original models and reveal a warming of 2 ºC to 6 ºC, along with decreasing precipitation in western Iberia after 2040. This approach offers key regional climate change information for adaptation strategies in the region.
This study uses deep learning (DL) to downscale global climate models for the Iberian Peninsula....
