Regional Climate Model Emulation with Diffusion Approaches: What is the Added Value of Generative Machine Learning?
arXiv:2606.14570v1 Announce Type: cross Abstract: Emulators provide a cost-effective alternative to regional climate models (RCMs) by capturing their dynamical downscaling function. They link large-scale predictors simulated by global climate models (GCMs) to RCM-simulated high-resolution fields of the target variable, here precipitation. Machine learning methods, typically deep learning, are cheaper than running RCMs in computation time and energy. Among them, generative models are appealing because they can simulate ensembles of local high-resolution fields consistent with the predictors. Th
The increasing computational demands of climate modeling and the rapid advancements in generative AI are converging, making these approaches feasible and efficient today.
This development allows for faster, more cost-effective, and higher-resolution climate simulations, which are critical for adaptation planning and scientific research.
Traditional RCMs can be emulated with significantly less computational time and energy, enabling more iterations and broader accessibility of downscaled climate data.
- · Climate scientists
- · Environmental research institutions
- · Deep learning researchers
- · Policymakers
- · Traditional RCM operators (less demand for raw compute time)
- · Energy-intensive supercomputing centers (for certain RCM tasks)
More accurate and faster regional-scale climate projections become available for a wider range of users.
Improved predictive capabilities could lead to better infrastructure planning and disaster preparedness in climate-vulnerable regions.
The reduced carbon footprint of climate modeling could subtly contribute to overall energy efficiency efforts in scientific research.
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