In this study, after developing a method for predicting full bloom dates of 'Kyoho' grape by incorporating 'Kyoho' growth characteristics at multiple sites in Japan into one model, we evaluated the prediction accuracy of the model. Using data of bud break dates and full bloom dates of 'Kyoho' at 14 public test sites from 2000-2018, we investigated the grape development characteristics at the sites. The conventional effective accumulated temperature model relies on the assumption of a linear relation between temperature and grape development. However, based on results of grape development characteristic analyses, we formulated new prediction models that include the following considerations: 1) a nonlinear relation between temperature and grape development, 2) effects of solar radiation on grape development, and 3) relations between Day of Year (DOY) of bud break date and grape development. Prediction analyses of two types were applied to full bloom dates at the 14 sites using the effective accumulated temperature model and our model, which includes the three considerations above simultaneously. For the first analysis, after estimating the model parameters for each site using data observed at that site, we predicted full bloom dates there. Evaluation of the prediction accuracy using leave-one-out cross validation revealed that the average root mean square error (RMSE) for all sites was 2.24 days for the effective accumulated temperature model. For our model, it was 2.06 days. The model had higher prediction accuracy at multiple sites. For the second analysis, we used the model parameters for each site based on data collected at all sites except the site in question. The average RMSE for all sites was 2.76 days for the effective accumulated temperature model and 2.54 days for our model. These results suggest that our model of 'Kyoho' growth characteristics at multiple sites can predict full bloom dates with higher accuracy than the effective accumulated temperature model.