Subseasonal Prediction of Tropical Cyclone Precipitation

Abstract The accurate prediction of tropical cyclone precipitation (TCP) at an extended range could be crucial to mitigate the impacts of TC-related flooding. This study examines probabilistic predictions of weekly accumulated TCP and total precipitation using 11 subseasonal forecast systems. Raw, uncalibrated, categorical forecasts of basinwide TCP are only skillful in the ECMWF model and only up to 15 days in advance, except in the northern Indian Ocean and the South Pacific where ECMWF is not skillful even at short leads. Calibration, through linear regression, improves forecasts and makes several forecast systems [Goddard Earth Observing System (GEOS) and UKMO] skillful up to 15 days in advance but only in some basins. In most models and basins, such as the GEOS model in the Atlantic basin, the bias in the forecast probability of TC occurrence is the main factor driving biases in TCP and decreasing forecast skill. At the regional scale, calibrated ECMWF forecasts are skillful beyond 15-day leads and globally. The poor prediction of TCP in raw forecasts is shown to affect total precipitation prediction skill. Therefore, biases in the TC occurrence probability forecast are the leading cause of low skill of TCP and may play a role in the skill of total precipitation. Significance Statement Tropical cyclone (TC)-related flooding is a significant hazard, and accurate extended-range predictions of tropical cyclone precipitation (TCP) are vital for risk mitigation efforts. This study assesses 11 subseasonal forecast systems, finding that uncalibrated models struggle with TCP prediction, with only the ECMWF model being skillful beyond 15 days in some basins. Calibration improves forecast skill, particularly for models like GEOS and UKMO, but biases in TC occurrence forecasts remain a major challenge. These biases reduce TCP prediction skill and affect total precipitation forecasts. Improving TCP prediction could significantly enhance preparedness for TC-related flooding events in key regions of the tropics.