Understanding the response of agriculture to heat and moisture stress is essential to adapt food systems under climate change. Although evidence of crop yield loss with extreme temperature is abundant, disentangling the roles of temperature and moisture in determining yield has proved challenging, largely due to limited soil moisture data and the tight coupling between moisture and temperature at the land surface. Here, using well-resolved observations of soil moisture from the recently launched Soil Moisture Active Passive satellite, we quantify the contribution of imbalances between atmospheric evaporative demand and soil moisture to maize yield damage in the US Midwest. We show that retrospective yield predictions based on the interactions between atmospheric demand and soil moisture significantly outperform those using temperature and precipitation singly or together. The importance of accounting for this water balance is highlighted by the fact that climate simulations uniformly predict increases in atmospheric demand during the growing season but the trend in root-zone soil moisture varies between models, with some models indicating that yield damages associated with increased evaporative demand are moderated by increased water supply. A damage estimate conditioned only on simulated changes in atmospheric demand, as opposed to also accounting for changes in soil moisture, would erroneously indicate approximately twice the damage. This research demonstrates that more accurate predictions of maize yield can be achieved by using soil moisture data and indicates that accurate estimates of how climate change will influence crop yields require explicitly accounting for variations in water availability.

了解农业对热和水分胁迫的反应对于适应气候变化下的粮食系统至关重要。尽管极端温度导致作物产量损失的证据很多，但事实证明，弄清温度和水分在决定产量中的作用具有挑战性，这主要是由于土壤水分数据有限以及地表水分和温度之间的紧密耦合。在这里，我们利用最近发射的土壤水分主动被动卫星对土壤水分的高分辨率观测，量化了大气蒸发需求和土壤水分之间的不平衡对美国中西部玉米产量损害的贡献。我们表明，基于大气需求和土壤水分之间相互作用的回顾性产量预测明显优于单独或同时使用温度和降水的预测。气候模拟一致预测生长季节大气需求增加但根区土壤水分趋势因模型而异，一些模型表明产量损失与蒸发增加有关需求因供水增加而减少。仅以大气需求的模拟变化为条件的损害估计，而不是同时考虑土壤水分的变化，将错误地指示大约两倍的损害。