Using results from a factor analysis regionalization of nontropical storm convective rainfall over the island
of Puerto Rico, a statistical methodology is investigated for its potential to forecast rain events over limited
areas. Island regionalization is performed on a 15-yr dataset, while the predictive model is derived from 3 yr
of surface and rainfall data. The work is an initial attempt at improving objective guidance for operational
rainfall forecasting in Puerto Rico. Surface data from two first-order stations are used as input to a partially
adaptive classification tree to predict the occurrence of heavy rain. Results from a case study show that the
methodology has skill above climatology—the leading contender in such cases. The algorithm also achieves
skill over persistence. Comparisons of forecast skill with a linear discriminant analysis suggest that classification
trees are an easier and more natural way to handle this kind of forecast problem. Synthesis of results confirms
the notion that despite the very local nature of tropical convection, synoptic-scale disturbances are responsible
for prepping the environment for rainfall. Generalizations of the findings and a discussion of a more realistic
forecast setting in which to apply the technology for improving tropical rainfall forecasts are given.