Global warming has modified the phenology of deciduous species. Temperature during the dormancy phase modulates the timing of bloom in temperate trees. Chill and heat requirements represent the climatic needs of trees during dormancy. Partial Least Squares (PLS) regression allows delineating chilling and forcing phases, which in turn allows estimating the trees’ requirements. However, PLS regression requires long-term phenology and weather data, which are scarce in many growing regions. In a two-year experiment, we generated long-term phenology data by exposing potted trees to distinct environments during winter. We obtained records for 66 and 32 experimental seasons in apple and pear, respectively. We recorded tree phenology and hourly temperature. Through PLS regression methods, we determined the impacts of inter-seasonal variation on the outputs, esti mated species’ dormancy phases and needs (in Chill Portions – CP and Growing Degree Hours – GDH), and assessed the relationship between bloom and temperature during the chilling and forcing phases. Results suggest inter-seasonal variation may be more important than number of seasons for producing valuable outputs. We delineated the chilling phase from October 19 to January 04 for apple and October 19 to December 27 for pear. The forcing period for both species was January 16 – March 26. Median chill and heat requirements were estimated as 43 CP and 14,845 GDH for apple and 31 CP and 11,816 GDH for pear. Bloom was modulated by temperature during both phases under warm conditions. In cold scenarios, bloom was mostly defined by tem peratures during the forcing phase. We expanded the reach of the PLS regression method and made it applicable for cultivars lacking long-term phenology data. Our approach helps dormancy researchers improve their pro cedures to analyze species’ responses under possible future conditions. This work may assist farmers and orchard managers in adapting their orchards to face future challenges.