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In climate science and meteorology, high-resolution local precipitation (rainand snowfall) predictions are limited by the computational costs ofsimulation-based methods. Statistical downscaling, or super-resolution, is acommon workaround where a low-resolution prediction is improved usingstatistical approaches. Unlike traditional computer vision tasks, weather andclimate applications require capturing the accurate conditional distribution ofhigh-resolution given low-resolution patterns to assure reliable ensembleaverages and unbiased estimates of extreme events, such as heavy rain. Thiswork extends recent video diffusion models to precipitation super-resolution,employing a deterministic downscaler followed by a temporally-conditioneddiffusion model to capture noise characteristics and high-frequency patterns.We test our approach on FV3GFS output, an established large-scale globalatmosphere model, and compare it against six state-of-the-art baselines. Ouranalysis, capturing CRPS, MSE, precipitation distributions, and qualitativeaspects using California and the Himalayas as examples, establishes our methodas a new standard for data-driven precipitation downscaling.

Precipitation Downscaling with Spatiotemporal Video Diffusion