Dynamical-generative downscaling of climate model ensembles.

ABSTRACT: Regional high-resolution climate projections are crucial for many applications, such as agriculture, hydrology, and natural hazard risk assessment. Dynamical downscaling, the state-of-the-art method to produce localized future climate information, involves running a regional climate model (RCM) driven by an Earth System Model (ESM), but it is too computationally expensive to apply to large climate projection ensembles. We propose an approach combining dynamical downscaling with generative AI to reduce the cost and improve the uncertainty estimates of downscaled climate projections. In our framework, an RCM dynamically downscales ESM output to an intermediate resolution, followed by a generative diffusion model that further refines the resolution to the target scale. This approach leverages

SUBMITTER: Lopez-Gomez I 

PROVIDER: S-EPMC12054837 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature