This study assessed the flowering-date forecast skill of cherry blossom in Tokyo from 2018 to 2023 using seasonal ensemble forecasts from three numerical weather prediction centers: the Deutscher Wetterdienst, the European Centre for Medium-Range Weather Forecasts, and the Météo-France. First, the optimal seven parameters used in the flowering-date estimation model, developed by Maruoka and Itoh (2009), were determined for Tokyo, based on the period from 1994 to 2017, during which the estimation bias was ±1.91 days. Then, flowering dates were predicted using bias-corrected seasonal ensemble forecast of 2 m temperature as a model input. The root-mean-square errors for the flowering-date forecasts initialized on 1st January, February, and March, averaged over all ensemble members, were about ±8.0 days, ±6.2 days, and ±2.3 days, respectively. The best- or worst-performing center is dependent on the specific cases. The grand ensemble forecast, comprising all forecasts from all single-center ensembles, showed better performance in predicting flowering dates of cherry blossoms than the single-center ensemble forecasts alone. These results suggest that the grand ensemble approach at seasonal timescales holds potential for predicting of the growth of flowers and fruits.