Long-Range Distillation: Distilling 10,000 Years of Simulated Climate into Long Timestep AI Weather Models
Researchers introduced long-range distillation, a method where a short-step AI model (teacher) generates massive synthetic climate data over 10,000 years using DLESyM. A separate, long-timestep student model is then trained on this synthetic data to forecast directly at long lead times. This approach shows that training on huge synthetic datasets significantly improves accuracy for subseasonal-to-seasonal (S2S) weather forecasts in the real world.