The Philippine springtime (February-April) sub-seasonal rainfall extremes and extended-range forecast skill assessment using the S2S database

During the first half month of April 2022, the Philippines experienced severe disasters associated with the weak but deadly tropical storm Megi that caused 214 deaths and two sunken ships. This prompted us to investigate the extended-range prediction skill of the springtime Philippine sub-seasonal scale rainfall extremes in the subseasonal-to-seasonal (S2S) prediction database. The results suggest that the S2S models can well predict the extremity of the 2022 springtime sub-seasonal peak rainfall event (SPRE) ten days ahead. In addition to the La Nin & SIM;a sea surface temperature anomalies, this prolonged rainfall event, from March 26 -April 14, 2022, was associated with an anomalous cyclonic circulation straddled over the South China Sea (SCS) and the Philippine Sea and persisted for two weeks. The strong relationship between the El Nin & SIM;o and Southern Oscillation (ENSO) and the springtime (February-April) rainfall variability in the Philippines is clearly revealed in the analysis of 25 years of observational and hindcast data. The extremely wet SPREs tend to occur during the La Nin & SIM;a springs, while the extremely dry SPREs tend to occur during the El Nin & SIM;o springs. The Madden-Julian oscillation (MJO) and equatorial Rossby (ER) waves that are capable of modulating the sub-seasonal rainfall extremes were weak when the deadly SPRE occurred in April 2022. Thus, the extended-range forecast skill of this example can be interpreted as the baseline skill of the current S2S prediction revealed in the multi-model database. The findings suggest that the SPRE is a useful item to be included in the operational forecast as potential opportunities to harness the benefits of S2S prediction and applications.

Automated summary

During the first half month of April 2022, the Philippines experienced severe disasters associated with tropical storm Megi, resulting in numerous deaths and sunken ships. The study found that S2S models have good predictive skills for extreme rainfall events ten days in advance. The findings also revealed a strong relationship between ENSO and springtime rainfall variability in the Philippines.