Extreme event attribution is used to evaluate the role of anthropogenic climate change in the occurrence or characteristics of extreme events. To be most useful, event attribution should be interpreted in the proper context and one part of this is understanding when event attribution results are robust. We investigate some factors contributing to the robustness of event attribution, by evaluating the roles of model choice, spatial scales, and ensemble sizes. We compare six single-model large ensembles to determine for which regions and variables there is agreement on at least two-fold changes in likelihood. As attribution of increases in the likelihood of daily temperature extremes is widespread by a 1 °C global warming level, we focus on precipitation extremes. In some regions, low signal to noise contributes to a lack of agreement on at least two-fold increases in likelihood in the current climate, but more model agreement in a 3 °C climate. In other regions, however, model differences remain, even at larger warming levels. Understanding where and when models do or do not agree on changes in large-scale precipitation extremes can be used to inform the design and interpretation of the local-scale analysis of specific events.

极端事件归因用于评估人为气候变化在极端事件发生或特征中的作用。为了最有用，应该在适当的上下文中解释事件归因，其中一部分是理解事件归因结果何时是稳健的。我们通过评估模型选择、空间尺度和集合大小的作用，研究了一些有助于事件归因稳健性的因素。我们比较了六个单一模型的大型集合，以确定哪些区域和变量在至少两倍的可能性变化上达成一致。由于每日极端温度的可能性增加的归因于 1°C 的全球变暖水平很普遍，因此我们关注极端降水。在某些地区，低信噪比导致在当前气候下的可能性至少增加两倍方面缺乏一致意见，但在 3°C 气候下更多的模型一致。然而，在其他地区，即使在更大的变暖水平下，模型差异仍然存在。了解模型在何时何地同意或不同意大尺度降水极值的变化可用于为特定事件的局部尺度分析的设计和解释提供信息。