Global gridded climate–crop model ensembles are increasingly used to make projections of how climate change will affect future crop yield. However, the level of certainty that can be attributed to such simulations is unknown. Here, using currently available geospatial datasets and a widely employed simulation procedure, we created a wheat model ensemble of 1,440 global simulations of 20 climate scenarios, 3 crop models, 4 parameterization strategies and 3 management inputs of sowing date. We quantified the contributions of climate, model, parameterization and management to the overall uncertainty to predicted responses of yield to warming, then related the results to the latitude of the grid cells. For all warming scenarios, the total uncertainty for mid- and high latitudes is much larger than for low latitudes. Uncertainty arising from crop models was larger than that from the other sources combined. Parameterizing crop models with grid-specific information on wheat cultivars tended to decrease the crop model uncertainty, particularly for low latitudes. Crop model improvements and better-quality spatial input data more closely representing the wide range of growing conditions around the world will be needed to reduce the uncertainty of climate change impact assessment of crop yields.

全球网格化的气候-作物模型集成越来越多地用于预测气候变化将如何影响未来的农作物产量。但是，可归因于此类模拟的确定性水平尚不清楚。在这里，使用当前可用的地理空间数据集和广泛使用的模拟程序，我们创建了一个由1440个全球模拟组成的小麦模型集合，其中包括20个气候情景，3个作物模型，4个参数化策略和3个播种日期管理输入。我们量化了气候，模型，参数化和管理对总体不确定性的贡献，以预测产量对变暖的响应，然后将结果与网格单元的纬度相关联。对于所有变暖情况，中高纬度地区的总不确定性要比低纬度地区大得多。作物模型引起的不确定性大于其他来源的不确定性。使用有关小麦品种的特定于网格的信息对作物模型进行参数化，往往会降低作物模型的不确定性，尤其是在低纬度地区。为了减少气候变化对作物单产影响的评估的不确定性，需要改进作物模型并提供更优质的空间输入数据，以更好地代表全球范围内的各种生长条件。