High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator

TL;DR

This paper presents a novel deep learning framework that enhances climate model resolution from 300 km to 25 km using diffusion-based generative models, enabling detailed regional climate impact assessments.

Contribution

It introduces a diffusion-based downscaling method that significantly improves spatial resolution of a lightweight climate emulator while maintaining accurate climatological statistics.

Findings

Successfully downscales climate data from 300 km to 28 km resolution.

Preserves coarse-grained dynamics and generates detailed climatological statistics.

Achieves accurate regional climate features validated by multiple metrics.

Abstract

The proliferation of data-driven models in weather and climate sciences has marked a significant paradigm shift, with advanced models demonstrating exceptional skill in medium-range forecasting. However, these models are often limited by long-term instabilities, climatological drift, and substantial computational costs during training and inference, restricting their broader application for climate studies. Addressing these limitations, Guan et al. (2024) introduced LUCIE, a lightweight, physically consistent climate emulator utilizing a Spherical Fourier Neural Operator (SFNO) architecture. This model is able to reproduce accurate long-term statistics including climatological mean and seasonal variability. However, LUCIE's native resolution (~300 km) is inadequate for detailed regional impact assessments. To overcome this limitation, we introduce a deep learning-based downscaling framework, leveraging probabilistic diffusion-based generative models with conditional and posterior sampling frameworks. These models downscale coarse LUCIE outputs to 25 km resolution. They are trained on approximately 14,000 ERA5 timesteps spanning 2000-2009 and evaluated on LUCIE predictions from 2010 to 2020. Model performance is assessed through diverse metrics, including latitude-averaged RMSE, power spectrum, probability density functions and First Empirical Orthogonal Function of the zonal wind. We observe that the proposed approach is able to preserve the coarse-grained dynamics from LUCIE while generating fine-scaled climatological statistics at ~28km resolution.