Statistically bias-corrected and downscaled climate models underestimate the adverse effects of extreme heat on US maize yields

Efforts to understand and quantify how a changing climate can impact agriculture often rely on bias-corrected and downscaled climate information, making it important to quantify potential biases of this approach. Here, we use a multi-model ensemble of statistically bias-corrected and downscaled climate models, as well as the corresponding parent models from the Coupled Model Intercomparison Project Phase 5 (CMIP5), to drive a statistical panel model of U.S. maize yields that incorporates season-wide measures of temperature and precipitation. We analyze uncertainty in annual yield hindcasts, finding that the CMIP5 models considerably overestimate historical yield variability while the bias-corrected and downscaled versions underestimate the largest weather-induced yield declines. We also find large differences in projected yields and other decision-relevant metrics throughout this century, leaving stakeholders with modeling choices that require navigating trade-offs in resolution, historical accuracy, and projection confidence. Historical annual maize yields in the U.S. are overestimated by CMIP5 models and underestimated by bias-corrected and downscaled models due to differences in temperature and precipitation hindcasts, according to a multi-model ensemble comparison.

Automated summary

A study compared different climate models and found that CMIP5 models overestimated historical yield variability, while bias-corrected and downscaled models underestimated weather-induced yield declines. There are significant differences in projected yields and other metrics throughout this century, leading to modeling choices that require balancing trade-offs in resolution, historical accuracy, and projection confidence.