Abstract
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.
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- Open access
- Green open access
Cite this article
APA 7
Lemaire, P. L., Schmidt, L., Suijker, W., Hernandez-Garcia, A., & Rolnick, D. (2026). RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling. https://omanscience.com/en/articles/rainatlas-a-multi-continental-dataset-for-precipitation-downscaling
MLA 9
Lemaire, Pierre-Louis, et al. "RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling." https://omanscience.com/en/articles/rainatlas-a-multi-continental-dataset-for-precipitation-downscaling.
Chicago (author–date)
Lemaire, Pierre-Louis, Luca Schmidt, Wietze Suijker, Alex Hernandez-Garcia, and David Rolnick. 2026. "RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling." https://omanscience.com/en/articles/rainatlas-a-multi-continental-dataset-for-precipitation-downscaling.
Harvard
Lemaire, P. L., Schmidt, L., Suijker, W., Hernandez-Garcia, A. and Rolnick, D. (2026) 'RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling', Available at: https://omanscience.com/en/articles/rainatlas-a-multi-continental-dataset-for-precipitation-downscaling.
Vancouver
Lemaire PL, Schmidt L, Suijker W, Hernandez-Garcia A, Rolnick D. RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling. https://omanscience.com/en/articles/rainatlas-a-multi-continental-dataset-for-precipitation-downscaling
IEEE
P. L. Lemaire, L. Schmidt, W. Suijker, A. Hernandez-Garcia, and D. Rolnick, "RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling," https://omanscience.com/en/articles/rainatlas-a-multi-continental-dataset-for-precipitation-downscaling.