Multivariate climate downscaling using deep learning models

Climate change is profoundly affecting ecosystems and societies. Impacts on hydrological regimes, water resources, and urban heatwaves are particularly important, emphasizing the need for a detailed understanding of these changes at local scales to inform effective adaptation strategies. Achieving this requires reliable, high-resolution projections of future climate conditions. However, current climate models operate at coarse spatial resolutions, limiting their ability to capture small-scale processes and extreme weather events. To bridge this gap, robust downscaling techniques are essential for refining the outputs of global and regional climate models.

We propose a multivariate super-resolution (SR) approach to downscale temperature and precipitation data in Switzerland to improve the representation of localized patterns, particularly in Alpine regions, while simultaneously capturing the interdependencies between temperature and precipitation, which are crucial for hydrological applications. We leverage advanced machine learning techniques, including Generative Adversarial Networks (GANs) and Diffusion models, to overcome the limitations of classical methods in capturing inter-variable dependencies. These models provide an ensemble framework, providing multiple possible realizations, to account for downscaling uncertainties, resulting in more robust and reliable outputs for impact modeling and decision-making. We test different loss functions, like a regional CRPS, to allow for variability in the generated meteorological fields.

We compare the performance of GANs and Diffusion models along with the differences between univariate and multivariate settings. Our approach includes applying a multivariate bias correction prior to downscaling. The downscaled results are compared to a setting based on univariate bias correction. Additionally, we present the pipeline, which integrates bias correction and downscaling and is intended to be open source.