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In this study, we examined the sensitivity of tropical cyclone (TC) characteristics and precipitation to the Cordillera Mountain Range (CMR) in Luzon, Philippines. Using the Weather Research and Forecasting (WRF) model, we simulated eight TCs with four different CMR orographic elevations: Control, Flat, Reduced, and Enhanced. We found that at significance level α = 0.05 , TC intensity significantly weakened as early as 21 h prior to landfall in the Enhanced experiment relative to the Control, whereas there was little change in the Flat and Reduced experiments. However, throughout the period when the TC crossed Luzon, we found no significant differences for TC movement speed and position in the different orographic elevations. When a TC made landfall, associated precipitation over the CMR increased as the mountain height increased. We further investigated the underpinning processes relevant to the effect of the CMR on precipitation by examining the effects of mountain slope, incoming perpendicular wind speed, and the moist Froude Number ( F w ). Compared with other factors, TC precipitation was most strongly correlated with the strength of approaching winds multiplied by the mountain slope, i.e., stronger winds blowing up steeper mountain slopes caused higher amounts of precipitation. We also found that individually, F w , mountain height, and upslope wind speeds were poor indicators of orographically induced precipitation. The effects of mountain range on TC rainfall varied with TC cases, highlighting the complexity of the mountain, wind, and rainfall relationship. [ABSTRACT FROM AUTHOR]

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]