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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2603.03838 |
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| _version_ | 1866912942163755008 |
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| author | Larsson, Erik Fuentes-Franco, Ramon Ivanov, Mikhail Lindsten, Fredrik |
| author_facet | Larsson, Erik Fuentes-Franco, Ramon Ivanov, Mikhail Lindsten, Fredrik |
| contents | Global climate projections rely on computationally demanding Earth System Models (ESMs), which are typically limited to coarse spatial resolutions due to their high cost. To obtain high-resolution projections for regions of interest, it is common to use Regional Climate Models (RCMs), which are driven by data produced by ESMs as boundary conditions. While more efficient than running ESMs at fine resolution, RCMs remain expensive and restrict the size of ensemble simulations. Inspired by recent advances in probabilistic machine learning for weather and climate, we introduce a data-driven climate downscaling method based on stochastic interpolants. Our approach efficiently transforms coarse ESM output into high-resolution regional climate projections at a fraction of the computational cost of traditional RCMs. Through extensive validation, we demonstrate that our method generates accurate regional ensembles, enabling both improved uncertainty quantification and broader use of high-resolution climate information. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_03838 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Climate Downscaling with Stochastic Interpolants (CDSI) Larsson, Erik Fuentes-Franco, Ramon Ivanov, Mikhail Lindsten, Fredrik Atmospheric and Oceanic Physics Global climate projections rely on computationally demanding Earth System Models (ESMs), which are typically limited to coarse spatial resolutions due to their high cost. To obtain high-resolution projections for regions of interest, it is common to use Regional Climate Models (RCMs), which are driven by data produced by ESMs as boundary conditions. While more efficient than running ESMs at fine resolution, RCMs remain expensive and restrict the size of ensemble simulations. Inspired by recent advances in probabilistic machine learning for weather and climate, we introduce a data-driven climate downscaling method based on stochastic interpolants. Our approach efficiently transforms coarse ESM output into high-resolution regional climate projections at a fraction of the computational cost of traditional RCMs. Through extensive validation, we demonstrate that our method generates accurate regional ensembles, enabling both improved uncertainty quantification and broader use of high-resolution climate information. |
| title | Climate Downscaling with Stochastic Interpolants (CDSI) |
| topic | Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2603.03838 |