High-Resolution Climate Projections Using Diffusion-Based Downscaling of a Lightweight Climate Emulator
arXiv:2602.13416v2 Announce Type: replace 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 Sphe
The proliferation of advanced data-driven models in climate science, coupled with the need to overcome their computational and stability limitations, drives the development of more efficient solutions.
High-resolution climate projections are crucial for accurate long-term planning, infrastructure development, and resource management in the face of escalating climate change impacts.
The ability to generate detailed climate projections with reduced computational cost and improved stability will democratize access to critical climate data, enhancing climate modeling capabilities globally.
- · Climate scientists
- · Urban planners
- · Agricultural sector
- · Renewable energy companies
- · Fossil fuel industries (indirect)
- · Regions unprepared for climate shifts
More precise regional climate impact assessments will become available.
Improved climate data will inform more resilient infrastructure designs and better disaster preparedness strategies.
Enhanced climate prediction capabilities could accelerate policy adoption for climate adaptation due to clearer economic and social impacts.
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Read at arXiv cs.LG