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

• URL: http://arxiv.org/abs/2602.13416v1
• Date: Fri, 13 Feb 2026 19:36:46 GMT
• Title: High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator
• Authors: Haiwen Guan, Moein Darman, Dibyajyoti Chakraborty, Troy Arcomano, Ashesh Chattopadhyay, Romit Maulik,
• Abstract summary: We introduce a deep learning-based downscaling framework to generate climate models at 28km resolution.<n>These models are trained on approximately 14,000 ERA5 timesteps spanning 2000-2009 and evaluated on LUCIE predictions from 2010 to 2020.<n>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.
• Score: 1.9367648935513015
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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