Generative assimilation and prediction for weather and climate

Machine learning models have shown great success in predicting weather up to two weeks ahead, outperforming process-based benchmarks 1 , 2 , 3 , 4 , 5 . However, most existing approaches do not fully integrate data assimilation into the prediction workflow 6 , 7 , and often suffer from error accumulation in autoregressive computation, limiting their applicability to predictions beyond the medium range 8 and climate projections 9 . We introduce Generative Assimilation and Prediction (GAP), a unified deep generative framework that connects data assimilation, weather forecasting, and climate simulation through a shared probabilistic representation of atmospheric states. GAP learns the climatological distribution from reanalysis data, and adapts it to different tasks by incorporating observational, predictive, and forcing constraints. Evaluated across a range of weather-climate tasks, GAP produces probabilistic weather forecasts competitive with leading forecasting systems, using its own assimilated initial states derived from 5% synthetic perfect observations, with particular skill in capturing extreme events such as atmospheric rivers, heatwaves and the Madden-Julian Oscillation. At seasonal timescales, GAP shows promising predictive skill when constrained by oceanic boundary forcing. Moreover, GAP supports stable millennial-scale climate simulations that reproduce atmospheric variability from daily weather fluctuations to decadal oscillations. These results suggest that generative modeling of the climatological distribution offers a viable foundation for seamless weather and climate prediction, thereby opening a path toward integrated, observation-to-climate workflows.