Abstract. 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 unknown change in the mean of the distribution of the climate variable being predicted. 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 dataset of high resolution EURO-CORDEX simulations for different seasons, rainfall variables, 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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使用均方误差模型平均来提高EURO-CORDEX未来降雨气候估计的潜在准确性和可用性

摘要。可以通过将分布与气候模型预测集合的成员进行拟合来估计未来气候状态的概率。总体平均值的变化可以用作对预测的气候变量分布平均值的未知变化的估计。但是，围绕整体平均值变化的采样不确定性水平因情况而异，并且在某些情况下很大。我们比较了两种模型平均方法，这些方法考虑了分布拟合过程中整体平均值变化的不确定性。它们都涉及使用不确定性调整后的集合平均值的值对集合进行拟合分布，以尝试相对于使用未调整的集合均值来提高预测技巧。我们使用这两种方法，根据高分辨率的EURO-CORDEX模拟大型数据集（针对不同季节，降雨变量，RCP和时间点）来对未来降雨量进行预测。使用点和概率验证方法对集成进行交叉验证表明，在大多数情况下，基于调整后的集成度平均值的预测比基于未调整后的集成度平均值的预测具有更高的潜在准确性。它们的性能也比基于常规Akaike模型平均和统计测试的预测要好。在具有统计意义的情况和没有统计学意义的情况之间，对总体平均值的调整会连续变化。在我们测试的两种方法中，一种非常简单，另一种更为复杂，并且涉及使用贝叶斯后验求平均值。