Climate downscaling with likelihood functional flow matching

Zhi-Song Liu; Helmi Toropainen; Michael Boy

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arXiv · 2610.33482

Climate downscaling with likelihood functional flow matching

Zhi-Song Liu·Helmi Toropainen·Michael Boy

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Abstract

Decisions about heat, frost and flooding are made locally, yet reanalyses and climate models resolve tens of kilometres. Generative downscalers add kilometre-scale detail with uncertainty, but are tied to a grid, costly to sample and lack a tractable likelihood. Here we introduce CD-FFM, a functional flow-matching model that treats the high-resolution field as a continuous random field that one model can query at any location and resolution. Its velocity acts pointwise, so spatial variability comes from a noise prior fitted to the observed spectrum and every observation has an exact likelihood. Downscaling ERA5 to 1 km daily temperature over Fennoscandia, CD-FFM lowers the continuous ranked probability score by 27-31% against regression and 12-18% against a forty-times larger neural operator in each of three test years, and outperforms a CorrDiff-style diffusion model while sampling about fifty times faster. The same model transfers to precipitation and makes large kilometre-scale ensembles affordable.

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Zhi-Song Liu, Helmi Toropainen, Michael Boy. 2026-10-24. Climate downscaling with likelihood functional flow matching. https://arxiv.org/abs/2610.33482

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