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Premise of the Study Predicting the flowering times of angiosperm taxa is a goal of mounting importance in the face of future climate change, with applications not only in plant biology and ecology, but also horticulture, agriculture, and invasive species management. To date, no tool is available to facilitate predictions of flowering phenology using multivariate phenoclimatic models. Such a tool is needed by researchers and other stakeholders who need to predict phenological activity, but are unfamiliar with phenoclimate modeling techniques. PhenoForecaster allows users of any background to conduct species-specific phenological predictions using an intuitive graphical interface and provides an estimate of each prediction's accuracy. Methods and Results Elastic net regression techniques were used to develop species-specific models capable of predicting the flowering dates of 2320 angiosperm species. Conclusions PhenoForecaster is the first stand-alone package to make phenological modeling directly accessible to users without the need for in-depth phenological observations.