Less is more: error-distance scaling relation for data-efficient kilometer-scale downscaling of extreme heat

Abstract: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-budget baselines degrade, and matches station observations during documented heat waves to within 1.8 K. Transfer to a new region degrades geographically; 11 days of local simulation cuts Vancouver's held-out error from 3.8 to 1.3 K. Training periods should span the target climate: the same accuracy for four times less simulation, putting kilometer-scale downscaling of extreme heat within reach of groups without large computing facilities.

Atmospheric and Oceanic Physics (physics.ao-ph); Machine Learning (cs.LG)

arXiv:2609.40140 [physics.ao-ph]

(or arXiv:2609.40140v1 [physics.ao-ph] for this version)

https://doi.org/10.48550/arXiv.2609.40140 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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