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Wietze Suijker

wietze.suijker@gmail.com
Independent visiting researcher
Supervisor
David Rolnick
Research Topics
Biodiversity
Machine Learning and Climate Change
Remote Sensing
Satellite Imagery
Website
GitHub

Publications

RainAtlas: A Multi-Continental Dataset for Precipitation Downscaling
P. Lemaire
Luca Schmidt
Wietze Suijker
Alex Hernández-García
David Rolnick
Extreme rainfall events are increasing in intensity and frequency as climate change accelerates. While kilometer-scale precipitation forecas… (see more)ts 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.
2026-09-29
arXiv (preprint)
doi.org
arxiv.org