  In climate science and meteorology, high-resolution local precipitation (rain
and snowfall) predictions are limited by the computational costs of
simulation-based methods. Statistical downscaling, or super-resolution, is a
common workaround where a low-resolution prediction is improved using
statistical approaches. Unlike traditional computer vision tasks, weather and
climate applications require capturing the accurate conditional distribution of
high-resolution given low-resolution patterns to assure reliable ensemble
averages and unbiased estimates of extreme events, such as heavy rain. This
work extends recent video diffusion models to precipitation super-resolution,
employing a deterministic downscaler followed by a temporally-conditioned
diffusion model to capture noise characteristics and high-frequency patterns.
We test our approach on FV3GFS output, an established large-scale global
atmosphere model, and compare it against six state-of-the-art baselines. Our
analysis, capturing CRPS, MSE, precipitation distributions, and qualitative
aspects using California and the Himalayas as examples, establishes our method
as a new standard for data-driven precipitation downscaling.
