Weather and Climate Extremes (Dec 2021)
Improvements in subseasonal forecasts of rainfall extremes by statistical postprocessing methods
Abstract
End users of seasonal rainfall forecasts demand not only skilful forecasts of rainfall totals but also skilful forecasts of rainfall extremes. For better forecasts of rainfall extremes, the copula-based postprocessing (CPP) method, which was originally designed for forecasting rainfall totals, is modified with a hybrid probability distribution to model low-to-medium and heavy rainfall separately and to allow the forecast of extreme rainfall events that have never occurred in observed records. A case study for 17 rainfall stations in Queensland, Australia is carried out to test the forecast performance of the modified CPP to postprocess the raw Australian Community Climate and Earth-System Simulator (ACCESS) Seasonal model (ACCESS-S) seasonal rainfall forecast at a daily scale. The modified CPP improves the overall skill of forecasting 12 rainfall indices from the raw forecast and outperforms two quantile mapping based methods in most cases. The use of the hybrid distribution leads to more promising forecast skill for heavy and very heavy rainfall related indices. The forecast skill decreases with longer lead times and the modified CPP leads to neutral forecasts (i.e. forecasts with skill similar to climatology forecasts) for most rainfall indices beyond 0-month lead time. The skill improvement has been found in all selected climate regions and initialisation dates (from the 1st day of each month), though more substantial improvement is observed in the rainfall stations within the tropical zone where the raw forecast is particularly unskilful.
