Articles | Volume 18, issue 6
https://doi.org/10.5194/gmd-18-2051-2025
© Author(s) 2025. 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-18-2051-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
01 Apr 2025
Development and technical paper |
| 01 Apr 2025
| 01 Apr 2025
Can AI be enabled to perform dynamical downscaling? A latent diffusion model to mimic kilometer-scale COSMO5.0_CLM9 simulations
Data Science for Industry and Physics, Fondazione Bruno Kessler, via Sommarive 18, 38123 Trento (TN), Italy
Gabriele Franch
Data Science for Industry and Physics, Fondazione Bruno Kessler, via Sommarive 18, 38123 Trento (TN), Italy
Marco Cristoforetti
Data Science for Industry and Physics, Fondazione Bruno Kessler, via Sommarive 18, 38123 Trento (TN), Italy
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- New horizons in statistical downscaling and AI approaches for sustainable km-scale climate simulations K. Chun et al. https://doi.org/10.1038/s41612-026-01424-6
- Probabilistic Super-Resolution for Urban Micrometeorology via a Schrödinger Bridge Y. Yasuda & R. Onishi https://doi.org/10.1007/s44393-025-00007-7
- Comparing Analog Ensemble and Corrective Diffusion for Western US precipitation downscaling from GraphCast W. Hu et al. https://doi.org/10.1080/20964471.2026.2665935
- 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
- A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation J. Schmidt et al. https://doi.org/10.1038/s41612-025-01157-y
- POTENTIAL OF SUPER-RESOLUTION FOR FLOOD IMPACT ASSESSMENT OF CLIMATE CHANGE A. OKAZAKI et al. https://doi.org/10.2208/jscejj.25-16127
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25 citations as recorded by crossref.
- Advances in atmospheric, oceanic, and coupled models for meteorological forecasting M. Waqas et al. https://doi.org/10.1016/j.nhres.2025.10.003
- Single-shot optically sectioned fluorescence endomicroscopy using unsupervised RCAN-CycleGAN C. Lin et al. https://doi.org/10.1364/OE.586459
- Reconstructing fine-scale 3D wind fields with terrain-informed machine learning C. Lin et al. https://doi.org/10.1038/s41467-026-70562-5
- Sea surface salinity downscaling using deep generative diffusion models E. Forestier et al. https://doi.org/10.5194/os-22-2357-2026
- Assessing the impact of AI-based meteorological postprocessing on seasonal hydrological forecasting skill in Mediterranean semi-arid basins D. De León Pérez et al. https://doi.org/10.1016/j.ejrh.2026.103535
- Vision transformers for multi-variable climate downscaling: emulating regional climate models with a shared encoder and multi-decoder architecture F. Merizzi & H. Loukos https://doi.org/10.1007/s00521-026-12066-3
- Statistical downscaling reproduces high-resolution ocean transport for particle tracking in the Bering Sea T. Kristiansen et al. https://doi.org/10.1038/s41598-026-37904-1
- Workshop report: EUMETNET - E-TREND joint workshop on postprocessing, forecasting and nowcasting for renewable energy J. Demaeyer et al. https://doi.org/10.1016/j.jemets.2026.100044
- A Review of Downscaling Multi-Source Satellite Data Fusion Methods B. Kumar et al. https://doi.org/10.1007/s12524-026-02445-7
- GPTCast: a weather language model for precipitation nowcasting G. Franch et al. https://doi.org/10.5194/gmd-18-5351-2025
- Bridging Global Climate Solutions and Local Realities: Evaluating Neural Networks for High-Resolution Downscaling D. Taniushkina et al. https://doi.org/10.1109/ACCESS.2026.3673693
- Air-Quality Forecasting Across Monitoring, Predictor, and Validation Regimes: A Systematic Mapping Review and Decision Framework E. Chianese & A. Riccio https://doi.org/10.3390/forecast8040072
- MESMER-RCM: a probabilistic climate emulator for regional warming projections H. Pan et al. https://doi.org/10.5194/npg-33-73-2026
- Comprehensive inter-comparison of generative AI models for super-resolution precipitation downscaling across hydroclimatic regimes S. Singh et al. https://doi.org/10.5194/gmd-19-7545-2026
- Bridging the weather and climate divide with artificial intelligence G. Camps-Valls et al. https://doi.org/10.1038/s41467-026-75787-y
- 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
- New horizons in statistical downscaling and AI approaches for sustainable km-scale climate simulations K. Chun et al. https://doi.org/10.1038/s41612-026-01424-6
- Probabilistic Super-Resolution for Urban Micrometeorology via a Schrödinger Bridge Y. Yasuda & R. Onishi https://doi.org/10.1007/s44393-025-00007-7
- Comparing Analog Ensemble and Corrective Diffusion for Western US precipitation downscaling from GraphCast W. Hu et al. https://doi.org/10.1080/20964471.2026.2665935
- 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
- A Generative Framework for Probabilistic, Spatiotemporally Coherent Downscaling of Climate Simulation J. Schmidt et al. https://doi.org/10.1038/s41612-025-01157-y
- POTENTIAL OF SUPER-RESOLUTION FOR FLOOD IMPACT ASSESSMENT OF CLIMATE CHANGE A. OKAZAKI et al. https://doi.org/10.2208/jscejj.25-16127
- HiFA-Diff: A High-Frequency-Aware Diffusion Framework for Meteorological Downscaling to 1-km Resolution M. Lyu et al. https://doi.org/10.1109/TGRS.2026.3722522
- Multimodal atmospheric super-resolution with deep generative models D. Chakraborty et al. https://doi.org/10.1088/3049-4753/ae286e
- TF-STNet: A Time–Frequency Dual-Branch Spatiotemporal Network for NWP-to-Station Bias Correction Z. Wang et al. https://doi.org/10.3390/e28091004
Saved (final revised paper)
Latest update: 16 Sep 2026
Short summary
High-resolution weather data are crucial for many applications, typically generated via resource-intensive numerical models through dynamical downscaling. We developed an AI model using latent diffusion models (LDMs) to mimic this process, increasing weather data resolution over Italy from 25 to 2 km. LDM outperforms other methods, accurately capturing local patterns and extreme events. This approach offers a cost-effective alternative, with potential disruptive application in climate sciences.
High-resolution weather data are crucial for many applications, typically generated via...
