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The paper introduces WxFlow, a conditional generative model based on flow matching, to probabilistically downscale coarse-resolution climate model outputs to fine-scale precipitation fields, incorporating high-resolution topography. WxFlow learns to map coarse climate data and fine topography to calibrated probabilistic ensembles, addressing the computational limitations of traditional dynamical downscaling methods like WRF. Applied to maximum 3-day snowfall in Alaska, WxFlow significantly outperforms lapse-rate-corrected bicubic downscaling, achieving an 87.8% improvement in spectral fidelity and lower Continuous Ranked Probability Scores, while generating ensembles rapidly.
Generate calibrated, high-resolution precipitation ensembles from coarse climate models on your laptop in seconds, outperforming traditional downscaling methods by a wide margin.
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 https://github.com/glide-ism/wrf-flow.
