We propose a novel mixture Generalized Pareto (MIXGP) model to calibrate extreme precipitation forecasts. This model is able to describe the marginal distribution of observed precipitation and capture the dependence between climate forecasts and the observed precipitation under suitable conditions. In addition, the full range distribution of precipitation conditional on grid forecast ensembles can also be estimated. Unlike the classical Generalized Pareto distribution that can only model points over a hard threshold, our model takes the threshold as a latent parameter. Tail behavior of both univariate and bivariate models are studied. The utility of our model is evaluated in Monte Carlo simulation study and is applied to precipitation data for the US where it outperforms competing methods.

中文翻译：

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用于双变量极值分析的贝叶斯半参数混合模型在降水预报中的应用

我们提出了一种新的混合广义帕累托 (MIXGP) 模型来校准极端降水预报。该模型能够描述观测降水的边际分布，捕捉气候预报与合适条件下观测降水之间的依赖关系。此外，还可以估计以网格预报集合为条件的降水全范围分布。与只能对超过硬阈值的点进行建模的经典广义帕累托分布不同，我们的模型将阈值作为潜在参数。研究了单变量和双变量模型的尾部行为。我们的模型的效用在 Monte Carlo 模拟研究中得到评估，并应用于美国的降水数据，其性能优于竞争方法。