Earth Sciences

Training machine learning models on climate model output yields skillful interpretable seasonal precipitation forecasts

P. B. Gibson, W. E. Chapman, et al.

Discover how machine learning models trained on extensive climate simulations can enhance seasonal forecasting accuracy for precipitation patterns in the western United States. This innovative research, led by Peter B Gibson and colleagues, shows that these models not only compete with traditional methods but also provide insights into the underlying physical processes.