Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift
For climate scientists needing robust high-resolution projections under non-stationary conditions, this work addresses a key temporal OOD challenge, though the improvement is incremental over existing methods.
This paper tackles temporal out-of-distribution shift in deep-learning-based climate downscaling, proposing a domain-adaptive framework that aligns historical and future climate distributions. The method consistently outperforms baselines, with largest gains under strong distribution shifts, and reduces upper-tail temperature bias.
Deep-learning-based climate downscaling aims to learn relationships from historical low-resolution (LR) and high-resolution (HR) climate data to generate HR climate projections. However, this setting faces a temporal out-of-distribution (OOD) challenge: models trained on historical data are commonly applied to future projections whose distributions may differ substantially from the training period. This study investigates temporal OOD shift for daily temperature downscaling over the Continental United States using paired LR-HR model simulations. We propose a temporal domain-adaptive downscaling framework that combines supervised HR reconstruction on historical data with domain alignment between historical and future climate distributions. Experiments across future validation periods show that the proposed domain-adaptive model consistently outperforms statistical and deep-learning-based bias-correction methods, with the largest gains occurring when the temporal distribution shift is strongest. Spatial analyses indicate stronger improvements over high-elevation and topographically complex regions, along with higher spatiotemporal correlation with the HR target. The extreme analysis shows that domain adaptation also reduces upper-tail temperature bias relative to the non-adaptive model. These results demonstrate that temporal domain adaptation can improve the robustness of HR climate projections under non-stationary climate conditions.