Why this breakthrough matters

Dynamical-generative downscaling represents a significant step towards obtaining comprehensive future regional climate projections at actionable scales below 10 km. It makes downscaling large ensembles of Earth system models computationally feasible — our study estimates computational cost savings of 85% for the 8-model ensemble tested, a figure that would increase for larger ensembles. The fast and efficient AI inference step is similar to how Google’s SEEDS and GenCast weather forecasting models operate, enabling a thorough assessment of regional environmental risk.

By providing more accurate and probabilistically complete regional climate projections at a fraction of the computational cost, dynamical-generative downscaling can dramatically improve environmental risk assessments. This enables better-informed decisions for adaptation and resilience policies across vital sectors like agriculture, water resource management, energy infrastructure, and natural hazard preparedness.