arXiv:2607.05645v1 Announce Type: new Abstract: 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.
The increasing sophistication of deep learning and the urgent need for accurate climate predictions are converging, making domain-adaptive techniques critical for real-world application.
Accurate, high-resolution climate downscaling is vital for infrastructure planning, agricultural resilience, and risk assessment, directly impacting economic stability and national security.
The ability to generate reliable future climate projections, accounting for temporal distribution shifts, improves the trustworthiness and utility of AI in critical scientific domains.
- · Climate scientists
- · Insurance industry
- · Agricultural sector
- · Urban planners
- · Legacy climate modeling approaches
- · Regions unprepared for climate shifts
Improved localized climate predictions for temperature and potentially other variables.
More robust and data-driven policy decisions regarding climate change adaptation and mitigation strategies.
Enhanced global collaboration on climate modeling and data sharing due to the demonstrated efficacy of advanced AI methods.
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Read at arXiv cs.LG