Limited reducibility of CMIP6 ensemble size to bracket crop climate impacts globally

Abstract. Global crop–climate impact studies using gridded, process-based models are essential for understanding how climate change may affect agricultural productivity. Due to high computational demand, these models are typically driven by a limited subset of projections from up to five global climate models (GCMs) from CMIP experiments. Yet, there is limited understanding of whether these subsets adequately capture variability in climate drivers within the whole ensemble and how the captured variability in drivers translates into crop yield impacts. Here, we analyze climate and crop yield projections from 29 GCMs contributing to CMIP6 ScenarioMIP that provide all required forcing variables and have been bias-adjusted and downscaled. To address computational constraints, we apply a high-performant crop model emulator to estimate yields of four staple crops across 150 k global locations under the four core emission pathways and for the whole climate ensemble from 1984–2099. We then aggregate results to major regions and use linear programming with the objective to minimize ensemble sizes while bracketing projected changes in temperature, precipitation, or crop yields. I.e., we identify the smallest GCM subsets that capture each variables’ full range. We find that ensemble sizes required to bracket ranges for individual variables increase from temperature (around 5–20 GCMs) to precipitation and further to crop yields (about 10–29 GCMs each), depending on the selection of crops and scenarios, and the strictness of the boundaries. Capturing yield responses across contrasting crops and emission pathways commonly requires the full ensemble. Notably, bracketing climate variables alone fails to capture the range of yield outcomes, highlighting the limited predictive value of individual climatic drivers. Our results emphasize the need for thorough contextualization of GCM selection in impact studies and suggest that emulators can fill a gap in complementing mechanistic model simulations with comprehensive climate uncertainty quantification.