Abstract:Precipitation in complex terrain is governed by orographic processes operating at scales of a few kilometers, yet climate models typically run at resolutions of 50--100~km where this topographic detail is absent. Dynamical downscaling with high-resolution regional models such as WRF can resolve these processes, but the computational cost -- months of wall-clock time per scenario -- precludes the large ensembles needed for uncertainty quantification. We present WxFlow, a conditional generative model based on flow matching that learns to map coarse-resolution climate model output and high-resolution topography to calibrated probabilistic ensembles of fine-scale precipitation fields. Applied to 4~km WRF simulations of maximum 3-day snowfall over southeast Alaska, WxFlow achieves 87.8\% improvement in spectral fidelity and dramatically lower Continuous Ranked Probability Scores relative to conventional lapse-rate-corrected bicubic downscaling, while generating 50-member ensembles in seconds on a laptop. Ensemble spread is spatially coherent and governed by topography, reflecting physically plausible uncertainty structure. All code is available at this https URL.
| Subjects: | Computational Physics (physics.comp-ph); Machine Learning (cs.LG); Atmospheric and Oceanic Physics (physics.ao-ph) |
| Cite as: | arXiv:2604.25172 [physics.comp-ph] |
| (or arXiv:2604.25172v1 [physics.comp-ph] for this version) | |
| https://doi.org/10.48550/arXiv.2604.25172 arXiv-issued DOI via DataCite (pending registration) |
From: Douglas Brinkerhoff [view email]
[v1]
Tue, 28 Apr 2026 03:21:23 UTC (1,804 KB)
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