Understanding how climate change may shape future agricultural production is essential as the world faces the challenge of feeding a growing population. Although this issue has received significant attention from the research community and policymakers, there are still many unanswered questions related to the susceptibility of these projections to model parametric uncertainty and the standard application of stationarity assumptions. In this study, we show how model parametric uncertainty affects our ability to make inferences from future food production projections. We focus on the dryland agricultural systems of the US Pacific Northwest as our case study area. We use a tightly coupled agro-hydrologic model VIC-CropSyst, as a representative of this class of biophysical models. Our diagnostic global sensitivity analysis identifies how influential factors vary across climatic zones during historical (1980-2010) and future (2054-2085) periods. The results show that dominant parametric controls change spatially across the hydro-climatic gradients. Our results also indicate that the implicit stationarity assumptions associated with using historically calibrated deterministic model parameterizations in projections can be problematic. Finally, we show that the dominant underlying parameterized biophysical processes that shape yields can change significantly by agro-climatic zones, types of projected future changes, and the source global circulation models. Perhaps most problematic, we demonstrate even modest treatments of parametric uncertainties can lead to drastically different projected yields.