Articles | Volume 15, issue 1
https://doi.org/10.5194/gmd-15-251-2022
© 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-15-251-2022
© 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
| 
13 Jan 2022
Model evaluation paper |
| 13 Jan 2022
| 13 Jan 2022
Convolutional conditional neural processes for local climate downscaling
Anna Vaughan
CORRESPONDING AUTHOR
Department of Engineering, University of Cambridge, Cambridge, UK
Will Tebbutt
Department of Engineering, University of Cambridge, Cambridge, UK
J. Scott Hosking
British Antarctic Survey, Cambridge, UK
The Alan Turing Institute, London, UK
Richard E. Turner
Department of Engineering, University of Cambridge, Cambridge, UK
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Cited
23 citations as recorded by crossref.
- End-to-end data-driven weather prediction A. Allen et al. https://doi.org/10.1038/s41586-025-08897-0
- Assessing statistical downscaling in Argentina: Daily maximum and minimum temperatures R. Balmaceda‐Huarte & M. Bettolli https://doi.org/10.1002/joc.7733
- spateGAN: Spatio‐Temporal Downscaling of Rainfall Fields Using a cGAN Approach L. Glawion et al. https://doi.org/10.1029/2023EA002906
- A two-step downscaling method for high-scale super-resolution of daily temperature — a case study of Wei River Basin, China X. Li et al. https://doi.org/10.1007/s11356-022-24422-6
- Projecting future climate extremes in the glacier-fed upper indus basin using machine learning based downscaling of CMIP6 GCMs M. Saleem et al. https://doi.org/10.1007/s00704-025-05793-5
- MoCoLSK: Modality-Conditioned High-Resolution Downscaling for Land Surface Temperature Q. Dai et al. https://doi.org/10.1109/TGRS.2025.3547945
- SPPM-FT: Multiscale Decoupled Transformer for Station-Specific Precipitation Forecasting Q. Liu et al. https://doi.org/10.1109/TGRS.2026.3683153
- 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
- Heterogeneous Spatiotemporal Graph Learning for Localized Sparse Meteorological Forecasting S. Li et al. https://doi.org/10.1109/TGRS.2025.3628905
- A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts L. Harris et al. https://doi.org/10.1029/2022MS003120
- Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks T. Asfaw & J. Luo https://doi.org/10.1007/s00376-023-3029-2
- Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5 K. Tazi et al. https://doi.org/10.5194/hess-28-4903-2024
- 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
- Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model A. Afshari et al. https://doi.org/10.3390/rs15184447
- Building and urban simulation under future climate: A novel statistical downscaling method for future hourly weather data generation P. Shen https://doi.org/10.1007/s12273-025-1277-z
- Characterization of Subsurface Hydrogeological Structures With Convolutional Conditional Neural Processes on Limited Training Data Z. Cui et al. https://doi.org/10.1029/2022WR033161
- Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Himalaya M. Girona-Mata et al. https://doi.org/10.5194/hess-29-3073-2025
- ResDeepD: A residual super-resolution network for deep downscaling of daily precipitation over India S. Mishra Sharma & A. Mitra https://doi.org/10.1017/eds.2022.23
- POINT-BY-POINT AND COMPLEX QUALITY METRICS IN ATMOSPHERE AND OCEAN RESEARCH: REVIEW OF METHODS AND APPROACHES V. Rezvov et al. https://doi.org/10.29006/1564-2291.JOR-2024.52(4).10
- Pointwise and Complex Quality Metrics in Atmospheric Modeling: Methods and Approaches V. Rezvov et al. https://doi.org/10.3103/S0027134924702229
- Precipitation prediction over the upper Indus Basin from large-scale circulation patterns using Gaussian processes K. Tazi et al. https://doi.org/10.1017/eds.2025.10020
- Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2) J. Svoboda et al. https://doi.org/10.5194/gmd-18-1829-2025
- A GNN-based station-level downscaling method for 2-m air temperature by considering region terrain H. Lai et al. https://doi.org/10.1016/j.rineng.2025.108825
23 citations as recorded by crossref.
- End-to-end data-driven weather prediction A. Allen et al. https://doi.org/10.1038/s41586-025-08897-0
- Assessing statistical downscaling in Argentina: Daily maximum and minimum temperatures R. Balmaceda‐Huarte & M. Bettolli https://doi.org/10.1002/joc.7733
- spateGAN: Spatio‐Temporal Downscaling of Rainfall Fields Using a cGAN Approach L. Glawion et al. https://doi.org/10.1029/2023EA002906
- A two-step downscaling method for high-scale super-resolution of daily temperature — a case study of Wei River Basin, China X. Li et al. https://doi.org/10.1007/s11356-022-24422-6
- Projecting future climate extremes in the glacier-fed upper indus basin using machine learning based downscaling of CMIP6 GCMs M. Saleem et al. https://doi.org/10.1007/s00704-025-05793-5
- MoCoLSK: Modality-Conditioned High-Resolution Downscaling for Land Surface Temperature Q. Dai et al. https://doi.org/10.1109/TGRS.2025.3547945
- SPPM-FT: Multiscale Decoupled Transformer for Station-Specific Precipitation Forecasting Q. Liu et al. https://doi.org/10.1109/TGRS.2026.3683153
- 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
- Heterogeneous Spatiotemporal Graph Learning for Localized Sparse Meteorological Forecasting S. Li et al. https://doi.org/10.1109/TGRS.2025.3628905
- A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts L. Harris et al. https://doi.org/10.1029/2022MS003120
- Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks T. Asfaw & J. Luo https://doi.org/10.1007/s00376-023-3029-2
- Downscaling precipitation over High-mountain Asia using multi-fidelity Gaussian processes: improved estimates from ERA5 K. Tazi et al. https://doi.org/10.5194/hess-28-4903-2024
- 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
- Statistical Downscaling of SEVIRI Land Surface Temperature to WRF Near-Surface Air Temperature Using a Deep Learning Model A. Afshari et al. https://doi.org/10.3390/rs15184447
- Building and urban simulation under future climate: A novel statistical downscaling method for future hourly weather data generation P. Shen https://doi.org/10.1007/s12273-025-1277-z
- Characterization of Subsurface Hydrogeological Structures With Convolutional Conditional Neural Processes on Limited Training Data Z. Cui et al. https://doi.org/10.1029/2022WR033161
- Probabilistic precipitation downscaling for ungauged mountain sites: a pilot study for the Hindu Kush Himalaya M. Girona-Mata et al. https://doi.org/10.5194/hess-29-3073-2025
- ResDeepD: A residual super-resolution network for deep downscaling of daily precipitation over India S. Mishra Sharma & A. Mitra https://doi.org/10.1017/eds.2022.23
- POINT-BY-POINT AND COMPLEX QUALITY METRICS IN ATMOSPHERE AND OCEAN RESEARCH: REVIEW OF METHODS AND APPROACHES V. Rezvov et al. https://doi.org/10.29006/1564-2291.JOR-2024.52(4).10
- Pointwise and Complex Quality Metrics in Atmospheric Modeling: Methods and Approaches V. Rezvov et al. https://doi.org/10.3103/S0027134924702229
- Precipitation prediction over the upper Indus Basin from large-scale circulation patterns using Gaussian processes K. Tazi et al. https://doi.org/10.1017/eds.2025.10020
- Towards deep-learning solutions for classification of automated snow height measurements (CleanSnow v1.0.2) J. Svoboda et al. https://doi.org/10.5194/gmd-18-1829-2025
- A GNN-based station-level downscaling method for 2-m air temperature by considering region terrain H. Lai et al. https://doi.org/10.1016/j.rineng.2025.108825
Saved (final revised paper)
Latest update: 09 Jun 2026
Short summary
We develop a new method for climate downscaling, i.e. transforming low-resolution climate model output to high-resolution projections, using a deep-learning model known as a convolutional conditional neural process. This model is shown to outperform an ensemble of baseline methods for downscaling daily maximum temperature and precipitation and provides a powerful new downscaling framework for climate impact studies.
We develop a new method for climate downscaling, i.e. transforming low-resolution climate model...
