Deep Learning has recently emerged as a perfect prognosis downscaling technique to compute high-resolution fields from large-scale coarse atmospheric data. Despite their promising results to reproduce the observed local variability, they are based on the estimation of independent distributions at each location, which leads to deficient spatial structures, especially when downscaling precipitation. This study proposes the use of generative models to improve the spatial consistency of the high-resolution fields, very demanded by some sectoral applications (e.g., hydrology) to tackle climate change.

翻译：深度学习近期作为一种完美预报降尺度技术出现，用于从大尺度粗分辨率大气数据计算高分辨率场。尽管这些技术在再现观测到的局部变异性方面取得了有希望的结果，但它们基于对每个位置独立分布的估计，这导致空间结构存在缺陷，尤其是在降尺度降水时。本研究提出使用生成模型来改进高分辨率场的空间一致性，这些一致性是一些部门应用（如水文）应对气候变化所迫切需要的。