Tropical cyclone (TC) genesis prediction at the extended‐range to subseasonal timescale (a week to several weeks) is a gap between weather and climate predictions. The current dynamical prediction systems and statistical models show limited skills in TC genesis forecasting at the lead time of 1–3 weeks. A hybrid dynamical‐statistical model is developed that reveals capability in predicting basin‐wide TC frequency in every 10‐day period over the western North Pacific at a 25‐day forecast lead, which is superior to the statistical and dynamical model‐based predictions examined in this study. In this hybrid model, the cyclogenesis counts for different TC clusters are predicted, respectively, using the statistical models in which the large‐scale predictors associated with intraseasonal oscillation evolutions are provided by a dynamical model. A probabilistic map of TC tracks at the subseasonal timescale is further predicted by incorporating the climatological probability of track distributions of these TC clusters.

中文翻译：

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推进北太平洋西部次季节热带气旋预报的混合动力-统计模型

热带气旋（TC）的成因预报是从季节到季节的扩展（一周到几周），这是天气和气候预测之间的差距。当前的动态预测系统和统计模型显示，在1-3周的前置时间内，TC起源预测的技能有限。建立了混合动力-统计模型，该模型显示了在25天的预报线索下预测北太平洋西部每10天周期内流域范围TC频率的能力，该模型优于所研究的基于统计和动力模型的预测在这个研究中。在此混合模型中，分别使用统计模型预测了不同TC簇的回生数，在该统计模型中，与一个季节内振荡演化相关的大型预测因子由一个动力学模型提供。