Highly resoluted and accurate daily precipitation data are required for impact models to perform adequately and to correctly measure high-risk events' impact. In order to produce such data, bias-correction is often needed. Most of those statistical methods correct the probability distributions of daily precipitation by modeling them using either empirical or parametric distributions. A recent semi-parametric model based on a penalized Berk-Jones (BJ) statistical test which allows for an automatic and personalized splicing of parametric and nonparametric has been developed. This method, called Stitch-BJ model, was found to be able to model daily precipitation correctly and showed interesting potential in a bias-correction setting. In the present study, we will consolidate these results by taking into account the seasonal properties of daily precipitation in an out-of-sample context, and by considering dry days probabilities in our methodology. We evaluate the performance of the Stitch-BJ method in this seasonal bias-correction setting against more classical models such as the Gamma, Exponentiated Weibull (ExpW), Extended Generalized Pareto (EGP) or empirical distributions. The Stitch-BJ distribution was able to consistently perform as well or better than all the other models over the validation set, including the empirical distribution, which is often used due to its robustness.

翻译：影响模型需要高分辨率且精确的日降水数据才能充分运行并准确衡量高风险事件的影响。为生成此类数据，通常需要进行偏差校正。多数统计方法通过经验分布或参数分布建模来校正日降水的概率分布。近期开发了一种基于惩罚性伯克-琼斯（BJ）统计检验的半参数模型，该模型能实现参数与非参数部分的自动个性化拼接。这种称为Stitch-BJ模型的方法被证明能准确模拟日降水，并在偏差校正场景中展现出良好潜力。本研究将通过考虑样本外情境中日降水的季节性特征，并在方法论中纳入干旱日概率，以巩固这些结论。我们评估了Stitch-BJ方法在季节性偏差校正场景中相对于伽马分布、指数化威布尔分布（ExpW）、扩展广义帕累托分布（EGP）及经验分布等经典模型的性能。在验证集上，Stitch-BJ分布始终表现出与所有其他模型相当或更优的性能，包括常因其稳健性而被采用的经验分布。