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Improving the seasonal prediction of tropical cyclone (TC) activity demands a robust analysis of the prediction skill and the inherent predictability of TC activity. Using the resampling technique, this study analyzes a state-of-the-art prediction system and offers a robust assessment of when and where the seasonal prediction of TC activity is skillful. We found that uncertainties of initial conditions affect the predictions and the skill evaluation significantly. The sensitivity of predictions to initial conditions also suggests that landfall and high-latitude activity are inherently harder to predict. The lower predictability is consistent with the relatively low prediction skill in these regions. Additionally, the lower predictability is largely related to the atmospheric environment rather than the sea surface temperature, at least for the predictions initialized shortly before the hurricane season. These findings suggest the potential for improving the seasonal TC prediction and will help the development of the next-generation prediction systems.

Plain Language Summary Dynamical seasonal prediction systems have recently shown great promises in predicting tropical cyclone (TC) activity. This study takes advantage of the new tool and evaluates the realized and the potential skills of the prediction. The evaluation reveals a caveat in earlier examinations of the TC prediction, which may significantly inflate or deflate the prediction skill. Our analysis takes account of the issue and offers a detailed view of when and where the TC prediction is skillful. The analysis also suggests that the realized and the potential prediction skill are relatively low for landfall activity and high-latitude activity. By analyzing simulated TC activity and its environmental controls, the study highlights the potential for improving seasonal TC prediction. The information is valuable to decision makers who manage risk in these regions, as well as the improvement of dynamical prediction systems.