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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. Plain Language Summary: Tropical cyclones (TCs) have huge impact in Southeast Asia (SEA). Seasonal forecasts of SEA peak‐season (July–September) TCs remains challenging for both statistical and dynamical models. In this paper, we introduce a novel statistical approach to sequentially identify independent predictors tailored for the frequency of SEA peak‐season TCs. The predictors are routinely constructed from far‐field sea surface temperatures and low‐level winds in the preceding‐season. The main concept in this approach is that each predictor represents a unique cluster of TC formation and propagation in the peak‐season. We also develop the approach to a hybrid prediction method by building the predictors from dynamical seasonal forecasts. The seasonal predictions based on the new approach perform much better than the current statistical and dynamical models. Such predictions can provide useful warning service for SEA TC activity 4–5 months ahead. Key Points: A new approach to identify independent predictors of Southeast Asia (SEA) tropical cyclone (TC) frequency is introducedThe preceding‐season predictors represent different clusters of TC formation and propagation through a cross‐season effectThe new method outperforms existing statistical and dynamical predictions for SEA TCs [ABSTRACT FROM AUTHOR]

The tropical west Pacific Ocean and the Philippines are often affected by tropical cyclones (TCs), with threats to human life and of severe economic damage. The performance of the Met Office global operational forecasts at predicting TC-related precipitation is examined between 2006 and 2017, the first time total TC rainfall has been analyzed in a long-term forecast dataset. All precipitation falling within 5° of a TC track point is assumed to be part of the TC rainbands. Forecasts are verified against TC tracks from the JRA-55 reanalysis and precipitation from TRMM 3B42. In composites from the forecasts, the total precipitation (TC and non-TC) is too high and the TC-related precipitation is too low, over both ocean and the Philippines. These biases exist all year-round and generally worsen with lead time, but have improved in recent years with upgrades to the forecasting system. Biases in TC-related precipitation in the Philippines are attributable mainly to TC lifetime being too short over land and ocean and (over land) possibly to individual TCs producing too little rain. There are considerable biases in predicted large-scale conditions related to TC intensification, particularly too little lower-troposphere relative humidity and too strong vertical wind shear. The shear appears to have little impact on the amount of TC precipitation, but dry biases in humidity are consistent with dry biases in TC rainfall. The forecast system accurately reproduces the impact of the MJO on TC precipitation, relative to the forecasts' own climatology, potentially providing the opportunity for predictability out to several weeks. [ABSTRACT FROM AUTHOR]