The first flowering date of apple trees is closely related to yield, making accurate prediction essential for orchard management. Many phenological models rely on overly simplified temperature assumptions that do not fully reflect the physiological processes of apple growth. The PhenoFlex model addresses this limitation by using an Sshaped transition to describe the shift from chill to heat accumulation. In this study, we coupled three years of phenology data (2020-2022) for "Red Fuji" apples with hourly 0.01 degrees gridded meteorological data from two Loess Plateau counties (Luochuan and Linyi). We evaluated the impact of data partitioning strategies (single-region, combined-region, individual-year, and combined-year) and partitioning based on average seasonal temperature gradients on the predictive capability of the PhenoFlex model. Results show that temperature gradients partitioning with single-region and combined-year calibration achieved the highest accuracy, with root mean square error (RMSE) of 2.19 days for Luochuan, 2.32 days for Linyi. Joint calibration across regions resulted in physiologically inconsistent representations of chill and heat interactions. In colder regions, flowering responses were primarily associated with reduced chill efficiency, an expanded overlap between chilling and forcing phases, and a pronounced increase in the duration of effective heat accumulation. In warmer regions, flowering advancement was primarily associated with an increased duration of effective heat accumulation. These results underscore the importance of incorporating temperature gradients into region-specific and multi-year calibration for the reliable application of PhenoFlex across heterogeneous climatic conditions, which is crucial for frost damage prevention and overall orchard management.