Transferring knowledge across regions: unsupervised domain adaptation for km-scale super-resolution

Ensuring that deep learning models generalize across distinct regimes remains a fundamental challenge in Earth system modeling. Due to the inherent violation of the independent and identically distributed (i.i.d.) assumption, models optimized for local conditions rarely exhibit robust performance on unseen domains. While Unsupervised Domain Adaptation (UDA) is a well-established technique for mitigating such distribution shifts in computer vision, its application to Earth system modeling remains underexplored. In this study we investigate the efficacy of UDA for the super-resolution of atmospheric fields, utilizing kilometer-scale COSMO simulations [1] and the RainShift benchmark dataset [2] to quantify model robustness across different regions. We apply residual learning to jointly super-resolve precipitation and surface pressure, incorporating static predictors such as topography. To quantify transferability, we propose a systematic framework that trains on source domains and evaluates on unseen target domains, treating spatial transfer as a proxy for model robustness under distribution shifts. We introduce a consistency metric to evaluate model adaptation by comparing mean performance on seen versus unseen domains. We assess a hierarchy of adaptation methods, ranging from simple regularization to physics-informed approaches. These include domain-specific regularization and distribution alignment methods, domain adversarial training, and geometry-robust training via group-equivariant convolutions. Preliminary results on the COSMO simulations demonstrate that even elementary adaptation strategies, such as dropout and data augmentation, improve cross-domain consistency. This work establishes a controlled setup for benchmarking generalization, suggesting that UDA offers a viable pathway to bridge the gap between locally trained models and global applicability.

[1]: Cui, R., Thurnherr, I., Velasquez, P., Brennan, K. P., Leclair, M., Mazzoleni, A., et al. (2025). A European hail and lightning climatology from an 11-year kilometer-scale regional climate simulation. Journal of Geophysical Research: Atmospheres, 130, e2024JD042828. https://doi.org/10.1029/2024JD042828

[2]: Paula Harder et al. RainShift: A Benchmark for Precipitation Downscaling Across Geographies. 2025. arXiv: 2507.04930 [cs.CV]. url: https://arxiv.org/abs/2507.04930.