发表机构
IVADO; Université de Montréal; McGill University; connAIx Research School, Tübingen AI Center, University of Tübingen; Cluster of Excellence Machine Learning, University of Tübingen; Mila - Quebec AI Institute（IVADO; 蒙特利尔大学; 麦吉尔大学; 蒂宾根大学connAIx研究学院，蒂宾根AI中心; 蒂宾根大学机器学习卓越集群; Mila-魁北克人工智能研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
RainAtlas是一个覆盖三大洲、含约21万对ERA5与观测数据的2公里网格多洲降水降尺度数据集，用于基准测试机器学习模型并揭示跨区域泛化差异。
AI 中文摘要
随着气候变化加速，极端降雨事件的强度和频率正在增加。虽然公里尺度的降水预报对于支持地方决策至关重要，但高分辨率降水观测数据的有限可用性阻碍了其准确性，尤其是在资源不足的地区。机器学习模型被广泛用于将降水数据降尺度到公里尺度，但其在未见地理区域的应用面临挑战。首先，跨区域处理原始高分辨率降水数据集需要大量的工程和领域专业知识。其次，跨区域的泛化仍然困难。为帮助克服这些障碍，我们发布了RainAtlas，一个大规模、机器学习就绪且多洲的降水降尺度数据集。覆盖三大洲，RainAtlas将异构的小时公里尺度观测数据统一到共同的2公里网格上。每个区域分区包含约210,000对对齐的低分辨率和高分辨率降水数据，分别来自ERA5再分析和直接观测。我们使用多种指标在RainAtlas上对最先进的基于机器学习的降尺度模型进行基准测试。我们的评估揭示了根据训练区域不同，域外泛化存在显著差异。这强调了跨区域、多源公里尺度评估的必要性，确立了RainAtlas作为降水降尺度研究中定位良好的基准。
英文摘要
Extreme rainfall events are increasing in intensity and frequency as climate change accelerates. While kilometer-scale precipitation forecasts are critical for supporting local decision-making, the limited availability of high-resolution precipitation observations hinders their accuracy, especially in under-resourced regions. Machine learning models are widely used to downscale precipitation data to km-scale, but their application to unseen geographies presents challenges. First, processing raw high-resolution precipitation datasets across regions requires significant engineering and domain expertise. Second, generalization across regions remains difficult. To help overcome these barriers, we release RainAtlas, a large-scale, ML-ready and multi-continental dataset for precipitation downscaling. Covering three continents, RainAtlas harmonizes heterogeneous hourly km-scale observations to a common 2-km grid. Each regional partition contains around 210,000 aligned low- and high-resolution precipitation pairs, respectively from ERA5 reanalysis and direct observations. We benchmark state-of-the-art ML-based downscaling models across RainAtlas using a wide range of metrics. Our evaluation reveals substantial variance in out-of-domain generalization depending on the training regions. This underscores the need for cross-regional, multi-source km-scale evaluation, establishing RainAtlas as a well-positioned benchmark for precipitation downscaling research.
