Quantitative simulation of precipitation in current climate has been an ongoing challenge for global climate models. Despite serious biases in correctly simulating probabilities of extreme rainfall events, model simulations under global warming scenarios are routinely used to provide estimates of future changes in these probabilities. To minimize the impact of model biases, past literature tends to evaluate fractional (instead of absolute) changes in probabilities of precipitation extremes under the assumption that fractional changes would be more reliable. However, formal tests for the validity of this assumption have been lacking. Here we evaluate two measures that address properties important to the correct simulation of future fractional probability changes of precipitation extremes, and that can be assessed with current climate data. The first measure tests climate model performance in simulating the characteristic shape of the probability of occurrence of daily precipitation extremes and the second measure tests whether the key parameter governing the scaling of this shape is well reproduced across regions and seasons in current climate. Contrary to concerns regarding the reliability of global models for extreme precipitation assessment, our results show most models lying within the current range of observational uncertainty in these measures. Thus, most models in the Coupled Model Intercomparison Project Phase 6 ensemble pass two key tests in current climate that support the usefulness of fractional measures to evaluate future changes in the probability of precipitation extremes.

当前气候下降水的定量模拟一直是全球气候模型面临的挑战。尽管在正确模拟极端降雨事件的概率方面存在严重偏差，但通常使用全球变暖情景下的模型模拟来提供这些概率未来变化的估计。为了使模型偏差的影响最小化，过去的文献倾向于在分数变化更为可靠的假设下评估极端降水概率的分数变化（而非绝对变化）。但是，缺乏对该假设有效性的正式测试。在这里，我们评估了两种措施，这些措施解决了对于正确模拟未来极端降水概率分数变化的重要属性，并且可以用当前的气候数据进行评估。第一种方法测试气候模型在模拟日降水极端事件发生概率的特征形状方面的性能，第二种方法测试控制这种形状缩放的关键参数是否在当前气候的区域和季节之间得到了很好的再现。与对用于极端降水评估的全球模型的可靠性的担忧相反，我们的结果表明，这些模型中的大多数模型都处于观测不确定性的当前范围内。因此，耦合模型比对项目第6阶段中的大多数模型都通过了当前气候下的两个关键测试，这些测试支持用分数法来评估未来极端降水概率的变化。