Abstract/Summary

While skilful forecasts of heavy rainfall are highly desirable for weather warnings and mitigating impacts, forecasting such events is notoriously difficult, even with the most advanced numerical weather prediction models, due to the strong dependence on convective-scale processes. The large-scale circulation, on the other hand, is typically more predictable. Weather patterns (WPs) are a set of circulation types obtained statistically that can be used to characterize regional weather and harness the predictability of the large-scale circulation. In this work we produce pattern-conditioned probabilistic rainfall forecasts by projecting the horizontal winds from the Met Office GloSea5 prediction system onto WPs and then using the observed relationship between each WP and rainfall estimated by satellite. The WPs are derived following a novel two-tier clustering technique: the WPs in the first tier represent planetary-scale variability, such as ENSO, while the WPs in the second tier capture synoptic-scale variability. We investigate WP predictability as well as the improvement in skill of sub-seasonal rainfall forecasts gained by this technique. GloSea5 predicts the WP occurrence with skill extending beyond lead day 10. The pattern-conditioned rainfall forecasts were evaluated against climatological forecasts and the model-simulated rainfall hindcasts. We show that pattern-conditioned forecasts are skilful and outperform the model-simulated rainfall hindcasts for lead times extending to days 10--20, depending on the specific exceedance criteria and region. Spatial aggregation leads to increased levels of skill, but not to a significant extension of the skilful prediction horizon. These results constitute a fundamental step for the development of sub-seasonal prediction systems for Southeast Asia.