Weather and Climate Foundation Models Enhance Subseasonal-to-Seasonal (S2S) Precipitation Prediction Using Multi-Source Satellite Observations

Subseasonal-to-Seasonal (S2S) forecasts, which are weather forecasts over a period spanning two weeks to two months, are challenging due to the position between short-term forecasts driven by initial conditions and seasonal forecasts governed by boundary conditions. Improving S2S forecasts skill to predict hydrological disasters like floods enables the establishment of disaster preparedness plans and reduces socioeconomic losses. Consequently, as the frequency of extreme precipitation events increases due to climate change, S2S forecasts are playing an increasingly vital role in early warning systems. However, S2S precipitation forecasts using traditional physics-based models are considered to have significant limitations due to errors arising from resolution, parameterization, and model uncertainty. Recently, interest has grown in whether data driven weather and climate models can bridge this forecasting gap.

Therefore, this study compares the precipitation forecasting performance of ECMWF and Korea Meteorological Administration (KMA) models with weather and climate foundation models to assess whether AI models can extend the predictability in the regions where S2S forecasts from traditional numerical weather prediction models are limited. Pre-trained foundation model and Multi-Source Weighted-Ensemble Precipitation (MSWEP) datasets are used for training a lightweight decoder to forecast precipitation from latent representations. We compare precipitation forecasts for nine years (2017-2025) with the MSWEP dataset, and analyze 2022 flood cases over Asia to evaluate the predictability of S2S for extreme weather events. We will show that a comparison of S2S precipitation forecast skill and extreme rainfall predictability between physics-based and AI models highlights the potential of S2S forecasts for early warning.