IPSL-AID: Generative Diffusion Models for Climate Downscaling from Global to Regional Scales
On the map: Diffusion models → Flow matching, time series and science
Abstract (arXiv)
Effective adaptation and mitigation strategies for climate change require high-resolution projections to inform strategic decision-making. Conventional global climate models, which typically operate at resolutions of 150 to 200 kilometers, lack the capacity to represent essential regional processes. IPSL-AID is a global to regional downscaling tool based on a denoising diffusion probabilistic model designed to address this limitation. Trained on ERA5 reanalysis data, it generates 0.25 degree resolution fields for temperature, wind, and precipitation using coarse inputs and their spatiotemporal context. It also models probability distributions of fine-scale features to produce plausible scenarios for uncertainty quantification. The model accurately reconstructs statistical distributions, including extreme events, power spectra, and spatial structures. This work highlights the potential of generative diffusion models for efficient climate downscaling with uncertainty
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Flow matching, time series and science, one of 14 directions in Diffusion models. Builds on 9 of its 25 references that are in the map. First posted on arXiv 23 Mar 2026.
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9 of 25 references.
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- 2023Residual Corrective Diffusion Modeling for Km-scale Atmospheric Downscaling24 Sep
- 2023Debias Coarsely, Sample Conditionally: Statistical Downscaling through Optimal Transport and Probabilistic Diffusion Models24 May
Other references · 7
- 2020Denoising Diffusion Probabilistic Modelscited by 21,076
- 2020Denoising Diffusion Implicit Modelscited by 9,781
- 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
- 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,255
- 2022FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operatorscited by 776
- 2024Generative Diffusion-based Downscaling for Climatecited by 25
- 2024Regional climate risk assessment from climate models using probabilistic machine learningcited by 7
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- 2023Generating High-Resolution Regional Precipitation Using Conditional Diffusion Model
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- 2026Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?
- 2024TAUDiff: Highly efficient kilometer-scale downscaling using generative diffusion models