Global climate change is causing shifts in phenology across multiple species. We use a geographically and temporally extensive data set of butterfly abundance across the state of Ohio to ask whether phenological change can be predicted from climatological data. Our focus is on growing degree days (GDD), a commonly used measure of thermal accumulation, as the mechanistic link between climate change and species phenology. We used simple calculations of median absolute error associated with GDD and an alternative predictor of phenology, ordinal date, for both first emergence and peak abundance of 13 butterfly species. We show that GDD acts as a better predictor than date for first emergence in nearly all species, and for peak abundance in more than half of all species, especially univoltine species. Species with less ecological flexibility, in particular those with greater dietary specialization, had greater predictability with GDD. The new method we develop for predicting phenology using GDD offers a simple yet effective way to predict species' responses to climate change.