Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches

Abstract High temperature and drought are widely known to cause a reduction of crop yield, but the simultaneously occurring risks in major producing countries and the associated uncertainty across various climate change scenarios remain unclear at the global scale. Here, we evaluate global maize yield loss risk (i.e., the probability of yield reduction by over 10% relative to historical trend yield during 1981–2010) across 30 hot and dry scenarios using regression, machine learning and process‐based models. Besides examining yield loss risk in a single country, we predict the potential risks simultaneously occurring in the top two and top ten producing countries. The three approaches agree on the aggravation of yield loss risk under dry and hot scenarios, but show large discrepancy in the magnitude and sensitivities. Specifically, 2°C warming alone could lead to a global yield loss risk of 73%, 100% and 62% based on regression, long‐short term memory (LSTM) and process‐based models, respectively, and warming‐induced risks can be further aggravated by droughts especially in process models. Global yield loss by over 10% would even become the new norm (i.e., yield loss probability is 100%) when temperature increases by over 2°C in some models. Importantly, the probabilities of yield loss simultaneously occurring in the top two countries (i.e., United States and China) and top ten countries are unexpectedly high and could even become 100% under extreme hot and dry scenarios. Our results highlight the large risks that future climate change may bring to multiple exporting and importing countries simultaneously, thus threating global food market and security. We also emphasize the important value of using different types of prediction approaches for yield projection under hot and dry scenarios, which enables more realistic estimation of uncertainty range than a single type of model.