Articles | Volume 15, issue 19
https://doi.org/10.5194/gmd-15-7353-2022
© 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-15-7353-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
05 Oct 2022
Development and technical paper |
| 05 Oct 2022
| 05 Oct 2022
Repeatable high-resolution statistical downscaling through deep learning
Dánnell Quesada-Chacón
CORRESPONDING AUTHOR
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
Klemens Barfus
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
Christian Bernhofer
Institute of Hydrology and Meteorology, Technische Universität Dresden, Dresden, Germany
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Cited
18 citations as recorded by crossref.
- Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain D. Quesada‐Chacón et al. https://doi.org/10.1029/2023EF003531
- Controlling ensemble variance in diffusion models: an application for reanalyses downscaling F. Merizzi et al. https://doi.org/10.1007/s00521-025-11709-1
- Downscaling of environmental indicators: A review S. Li et al. https://doi.org/10.1016/j.scitotenv.2024.170251
- A Closed-Loop Circular Regression Network for 2-m Air Temperature Downscaling Over Southwestern China G. Liu et al. https://doi.org/10.1109/TGRS.2024.3386908
- pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information D. Boateng & S. Mutz https://doi.org/10.5194/gmd-16-6479-2023
- Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review Y. Sun et al. https://doi.org/10.1016/j.isprsjprs.2023.12.011
- Machine Learning in Climate Downscaling: A Critical Review of Methodologies, Physical Consistency, and Operational Applications H. Najafi et al. https://doi.org/10.3390/w18020271
- A trend-preserving statistical downscaling framework and its application to China’s offshore wind field H. Wang et al. https://doi.org/10.1007/s10584-025-04056-6
- Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian https://doi.org/10.1007/s00382-024-07406-9
- Emulator Based Downscaling of Future Temperature by Evaluating Multiple Machine Learning Models in Peninsular Malaysia N. Khan et al. https://doi.org/10.1002/joc.70329
- Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches J. González‐Abad et al. https://doi.org/10.1029/2023MS003641
- Land–Water Allocation, Yield Stability, and Policy Trade-Offs Under Climate Change: A System Dynamics Analysis X. Jia & R. Zhang https://doi.org/10.3390/systems14040412
- Challenges of modelling climate change impacts on hydrology and water resources: AI is the game changer—a review C. Onyutha https://doi.org/10.1088/2752-5295/ae2a60
- Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al. https://doi.org/10.1016/j.acags.2024.100201
- Exploring super-resolution spatial downscaling of several meteorological variables and potential applications for photovoltaic power A. Damiani et al. https://doi.org/10.1038/s41598-024-57759-8
- Deep Learning-Based Evaluation of Offshore Wind Energy Resources in Southeastern China for the Future C. Lai et al. https://doi.org/10.3390/en19061447
- A Workflow for Benchmarking Added Value by New Statistical Downscaling Methods R. Wilby et al. https://doi.org/10.1142/S2345737625400020
- Repeatable high-resolution statistical downscaling through deep learning D. Quesada-Chacón et al. https://doi.org/10.5194/gmd-15-7353-2022
18 citations as recorded by crossref.
- Downscaling CORDEX Through Deep Learning to Daily 1 km Multivariate Ensemble in Complex Terrain D. Quesada‐Chacón et al. https://doi.org/10.1029/2023EF003531
- Controlling ensemble variance in diffusion models: an application for reanalyses downscaling F. Merizzi et al. https://doi.org/10.1007/s00521-025-11709-1
- Downscaling of environmental indicators: A review S. Li et al. https://doi.org/10.1016/j.scitotenv.2024.170251
- A Closed-Loop Circular Regression Network for 2-m Air Temperature Downscaling Over Southwestern China G. Liu et al. https://doi.org/10.1109/TGRS.2024.3386908
- pyESDv1.0.1: an open-source Python framework for empirical-statistical downscaling of climate information D. Boateng & S. Mutz https://doi.org/10.5194/gmd-16-6479-2023
- Deep learning in statistical downscaling for deriving high spatial resolution gridded meteorological data: A systematic review Y. Sun et al. https://doi.org/10.1016/j.isprsjprs.2023.12.011
- Machine Learning in Climate Downscaling: A Critical Review of Methodologies, Physical Consistency, and Operational Applications H. Najafi et al. https://doi.org/10.3390/w18020271
- A trend-preserving statistical downscaling framework and its application to China’s offshore wind field H. Wang et al. https://doi.org/10.1007/s10584-025-04056-6
- Multivariate bias correction and downscaling of climate models with trend-preserving deep learning F. Wang & D. Tian https://doi.org/10.1007/s00382-024-07406-9
- Emulator Based Downscaling of Future Temperature by Evaluating Multiple Machine Learning Models in Peninsular Malaysia N. Khan et al. https://doi.org/10.1002/joc.70329
- Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches J. González‐Abad et al. https://doi.org/10.1029/2023MS003641
- Land–Water Allocation, Yield Stability, and Policy Trade-Offs Under Climate Change: A System Dynamics Analysis X. Jia & R. Zhang https://doi.org/10.3390/systems14040412
- Challenges of modelling climate change impacts on hydrology and water resources: AI is the game changer—a review C. Onyutha https://doi.org/10.1088/2752-5295/ae2a60
- Current progress in subseasonal-to-decadal prediction based on machine learning Z. Shen et al. https://doi.org/10.1016/j.acags.2024.100201
- Exploring super-resolution spatial downscaling of several meteorological variables and potential applications for photovoltaic power A. Damiani et al. https://doi.org/10.1038/s41598-024-57759-8
- Deep Learning-Based Evaluation of Offshore Wind Energy Resources in Southeastern China for the Future C. Lai et al. https://doi.org/10.3390/en19061447
- A Workflow for Benchmarking Added Value by New Statistical Downscaling Methods R. Wilby et al. https://doi.org/10.1142/S2345737625400020
- Repeatable high-resolution statistical downscaling through deep learning D. Quesada-Chacón et al. https://doi.org/10.5194/gmd-15-7353-2022
Saved (final revised paper)
Latest update: 28 May 2026
Short summary
We improved the performance of past perfect prognosis statistical downscaling methods while achieving full model repeatability with GPU-calculated deep learning models using the TensorFlow, climate4R, and VALUE frameworks. We employed the ERA5 reanalysis as predictors and ReKIS (eastern Ore Mountains, Germany, 1 km resolution) as precipitation predictand, while incorporating modern deep learning architectures. The achieved repeatability is key to accomplish further milestones with deep learning.
We improved the performance of past perfect prognosis statistical downscaling methods while...
