Forests strongly affect Earth's carbon cycles, making our ability to forecast forest-productivity changes associated with rising temperatures and changes in precipitation increasingly critical. In this study, we model the influence of climate on annual radial growth using lodgepole pine (Pinus contorta) trees grown for 34 years in a large provenance experiment in western Canada. We use a random-coefficient modeling approach to build universal growth-trend response functions that simultaneously incorporate the impacts of different provenance and site climates on radial growth trends under present and future annual (growth-year), summer, and winter climate regimes. This approach provides new depth to traditional quantitative genetics population response functions by illustrating potential changes in population dominance over time, as well as indicating the age and size at which annual growth begins declining for any population growing in any location under any present or future climate scenario within reason, given the ages and climatic conditions sampled. Our models indicate that lodgepole pine radial-growth levels maximize between 3.9 degrees and 5.1 degrees C mean growth-year temperature. This translates to productivity declining by the mid-21st century in southern and central British Columbia (BC), while increasing beyond the 2080s in northern BC and Yukon, as temperatures rise. Relative to summer climate indices, productivity is predicted to decline continuously through the 2080s in all locations, while relative to winter climate variables, the opposite trend occurs, with the growth increases caused by warmer winters potentially offsetting the summer losses. Trees from warmer provenances, i.e., from the center of the species range, perform best in nearly all of our present and future climate-scenario models. We recommend that similar models be used to analyze population growth trends relative to annual and intra-annual climate in other large-scale provenance trials worldwide. An open-access growth-trend data set encompassing numerous biomes, species, and provenances would contribute substantially to predicting forest productivity under future-climate scenarios.