A New Approach to Skillful Seasonal Prediction of Southeast Asia Tropical Cyclone Occurrence

Abstract

Abstract Predicting the peak‐season (July–September) tropical cyclones (TCs) in Southeast Asia (SEA) several months ahead remains challenging, related to limited understanding and prediction of the dynamics affecting the variability of SEA TC activity. Here, we introduce a new statistical approach to sequentially identify mutually independent predictors for the occurrence frequency of peak‐season TCs in the South China Sea (SCS) and east of the Philippines (PHL). These predictors, which are identified from the preseason (April‐June) environmental fields, can capture the interannual variability of different clusters of peak‐season TCs, through a cross‐season effect on large‐scale environment that governs TC genesis and track. The physically oriented approach provides a skillful seasonal prediction in the 41‐year period (1979–2019), with r = 0.73 and 0.54 for SCS and PHL TC frequency, respectively. The lower performance for PHL TCs is likely related to the nonstationarity of the cross‐season TC‐environment relationship. We further develop the statistical approach to a hybrid method using the predictors derived from dynamical seasonal forecasts. The hybrid prediction shows a significant skill for both SCS and PHL TCs, for lead times up to four or 5 months ahead, related to the good performance of models for the sea surface temperatures and low‐level winds in the tropics. The statistical and hybrid predictions outperform the dynamical predictions, showing the potential for operational use.