Less is more: error-distance scaling relation for data-efficient kilometer-scale downscaling of extreme heat
Extreme heat is where urban adaptation needs kilometer-scale data the most, but the simulations training a downscaler can cost more than they save, and how much is needed has not been identified. We measured it with CASPER, a U-Net with a structure-preserving loss downscaling 32 km reanalysis to 1 km temperature, humidity and wind, across 24 configurations of one to eight months. Held-out error grows linearly with climatological distance to the training data, RMSE = 0.83 + 2.95 d, explaining 90% of its variance against 7% for volume and predicting unseen months in advance. On held-out extreme summer weeks CASPER preserves the fine-scale structure and cross-variable physics that matched-bu...
| Authors | Ahmed Marey, Henry Lu, Abhishek Gaur et al. |
|---|---|
| Primary category | physics.ao-ph |
| Published | 30 September 2026 |
| Last updated at source | 1 October 2026 |
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