Probabilities of future climate states can be estimated by fitting distributions to the members of an ensemble of climate model projections. The change in the ensemble mean can be used as an estimate of the change in the mean of the real climate. However, the level of sampling uncertainty around the change in the ensemble mean varies from case to case and in some cases is large. We compare two model-averaging methods that take the uncertainty in the change in the ensemble mean into account in the distribution fitting process. They both involve fitting distributions to the ensemble using an uncertainty-adjusted value for the ensemble mean in an attempt to increase predictive skill relative to using the unadjusted ensemble mean. We use the two methods to make projections of future rainfall based on a large data set of high-resolution EURO-CORDEX simulations for different seasons, rainfall variables, representative concentration pathways (RCPs), and points in time. Cross-validation within the ensemble using both point and probabilistic validation methods shows that in most cases predictions based on the adjusted ensemble means show higher potential accuracy than those based on the unadjusted ensemble mean. They also perform better than predictions based on conventional Akaike model averaging and statistical testing. The adjustments to the ensemble mean vary continuously between situations that are statistically significant and those that are not. Of the two methods we test, one is very simple, and the other is more complex and involves averaging using a Bayesian posterior. The simpler method performs nearly as well as the more complex method.

中文翻译：

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使用频率模型平均提高 EURO-CORDEX 对未来降雨气候估计的潜在准确性和可用性

可以通过将分布拟合到气候模型预测集合的成员来估计未来气候状态的概率。集合均值的变化可用作对真实气候均值变化的估计。然而，围绕整体均值变化的抽样不确定性水平因情况而异，在某些情况下很大。我们比较了两种模型平均方法，它们在分布拟合过程中考虑了集合均值变化的不确定性。它们都涉及使用集合平均值的不确定性调整值来拟合集合的分布，以尝试相对于使用未调整的集合平均值提高预测技能。我们使用这两种方法根据不同季节、降雨变量、代表性浓度路径 (RCP) 和时间点的高分辨率 EURO-CORDEX 模拟的大型数据集来预测未来的降雨量。使用点和概率验证方法在集合内进行交叉验证表明，在大多数情况下，基于调整后的集合均值的预测比基于未调整的集合均值的预测具有更高的潜在准确性。它们的性能也优于基于传统 Akaike 模型平均和统计检验的预测。对整体均值的调整在统计显着和不显着的情况之间不断变化。在我们测试的两种方法中，一种非常简单，另一种更复杂，涉及使用贝叶斯后验平均。