Forecasting spring phenology of temperate fruit trees is of high concern for orchard plannersand fruit producers, particularly in the context of climate change. Responding to this need, horticultural researchers have developed models to estimate chill and heat requirements and project dormancy release. Despite some successes in dormancy modeling, several shortcomings still hamper reliable forecasts. Many widely used models rely on oversimplified and inflexible assumptions and are neither validated nor parameterized for most species or cul tivars. More complex models are often poorly accessible due to a lack of guidance on calibration and application. Moreover, most approaches do not provide estimates of uncertainty. We aimed to develop a dormancy model that (a) is based on the best available biological understanding and experimental evidence on dormancy dy namics, (b) can flexibly adapt to species- and cultivar-specific physiology, (c) comes with a detailed description of the work-flow and (d) is open-source. The result is the new modeling framework PhenoFlex. It combines the Dynamic Model for chill accumulation with the Growing-Degree-Hours model for heat accumulation by a flexible transition. PhenoFlex is accompanied by a framework for calibrating the 12 model parameters. It is published as part of the chillR package, which contains a detailed vignette. We tested the predictive performance of PhenoFlex with 60 years of apple and pear bloom data and compared results to several benchmark models. With Root Mean Square Error values for projected bloom dates of 4.0 days for pears and 3.8 days for apples, PhenoFlex out performed all other models including the StepChill model (10.2 and 7.7 days, respectively), and a machine learning approach (5.6 and 6.3 days). Some temperature response dynamics appeared unrealistic, indicating the need for larger training datasets with more temperature variation. We hope that PhenoFlex will facilitate further research on the temperature response dynamics of temperate tree species.