📝 SummarXiv

Abstract

High-resolution climate projections are essential for local decision-making. However, available climate projections have low spatial resolution (e.g. 12.5 km), which limits their usability. We address this limitation by leveraging single-image super-resolution models to statistically downscale climate projections to 1-km resolution. Since high-resolution climate projections are unavailable for training, we train models on a high-resolution observational gridded data set and apply them to low-resolution climate projections. We propose a climate indicator-based assessment using observed climate indices computed at weather station locations to evaluate the downscaled climate projections without ground-truth high-resolution climate projections. Experiments on daily mean temperature demonstrate that single-image super-resolution models can downscale climate projections without increasing the error of climate indicators compared to low-resolution climate projections.