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Despite the improving techniques for seasonal prediction of tropical storm frequency, attention seems focused on accuracy rather than on forecast interpretation. This study aims to show how seasonal predictions from a hybrid model, i.e. statistical/dynamical model, can be interpreted with probability distributions. The tropical storm frequency in the western North Pacific is modeled with environmental predictors through multiple linear regression. For a demonstration of the probabilistic structure of the prediction result, the forty-two member ensemble predictions from the Glosea5 model for June–July–August in 2020 are used as the dynamical input. Rather than dealing with the expected frequency, this study introduces the predictive probability for a single value of the frequency. From as many probability distributions, a marginal probability distribution is obtained as the final predictive probability distribution. The probability distribution is then compared to the climatological reference by terciles. Additionally, predictive probability distributions made with the individual predictors provide helpful information on how each contributes to the final prediction. This probabilistic interpretation procedure is expected to be effectively used for improving any hybrid approach.