Mind the Residual Gap: Probabilistic Downscaling under Real-World Bias
Probabilistic downscaling models the conditional distribution of high-resolution fields given coarse inputs and is a core challenge in atmospheric science, climate modeling, and other multiscale physical systems. A widely used paradigm decomposes the problem into a deterministic mean predictor followed by a stochastic residual generator. While effective in idealized settings, this mean-residual approach frequently produces biased and underdispersive ensembles in real-world applications. We identify a fundamental source of this failure: residual target misspecification, where the residual distribution induced during training systematically differs from the correction distribution required ...
| Authors | Yujin Kim, Nidhi Soma, Sarah Dean |
|---|---|
| Primary category | cs.LG |
| Published | 29 June 2026 |
| Last updated at source | 27 September 2026 |
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