In climate science and meteorology, local precipitation predictions are limited by the immense computational costs induced by the high spatial resolution that simulation methods require. A common workaround is statistical downscaling (aka superresolution), where a low-resolution prediction is super-resolved using statistical approaches. While traditional computer vision tasks mainly focus on human perception or mean squared error, applications in weather and climate require capturing the conditional distribution of high-resolution patterns given low-resolution patterns so that reliable ensemble averages can be taken. Our approach relies on extending recent video diffusion models to precipitation superresolution: an optical flow on the high-resolution output induces temporally coherent predictions, whereas a temporally-conditioned diffusion model generates residuals that capture the correct noise characteristics and high-frequency patterns. We test our approach on X-SHiELD, an established large-scale climate simulation dataset, and compare against two state-of-the-art baselines, focusing on CRPS, MSE, precipitation distributions, as well as an illustrative case -- the complex terrain of California. Our approach sets a new standard for data-driven precipitation downscaling.

翻译：在气候科学与气象学中，局部降水预测受限于模拟方法所需高空间分辨率带来的巨大计算成本。一种常见的解决方案是统计降尺度（也称超分辨率），即利用统计方法将低分辨率预测提升为高分辨率。传统计算机视觉任务主要关注人类感知或均方误差，而天气与气候领域的应用则需要捕捉高分辨率模式在给定低分辨率模式条件下的条件分布，从而获取可靠的集合平均值。我们的方法将近期视频扩散模型扩展到降水超分辨率：高分辨率输出上的光流可生成时间一致预测，而时间条件扩散模型则生成残差以捕捉正确的噪声特征和高频模式。我们在权威的大规模气候模拟数据集X-SHiELD上测试了该方法，并与两个最先进的基线模型进行对比，评估指标涵盖CRPS、均方误差、降水分布以及一个典型案例——加利福尼亚州复杂地形区域。我们的方法为数据驱动的降水降尺度设立了新标准。