Climate model large ensembles are an essential research tool for analysing and quantifying natural climate variability and providing robust information for rare extreme events. The models simulated representations of reality are susceptible to bias due to incomplete understanding of physical processes. This paper aims to correct the bias of five climate variables from the CRCM5 Large Ensemble over Central Europe at a 3-hourly temporal resolution. At this high temporal resolution, two variables, precipitation and radiation, exhibit a high share of zero inflation. We propose a novel bias-correction method, VBC (Vine copula bias correction), that models and transfers multivariate dependence structures for zero-inflated margins in the data from its error-prone model domain to a reference domain. VBC estimates the model and reference distribution using vine copulas and corrects the model distribution via (inverse) Rosenblatt transformation. To deal with the variables' zero-inflated nature, we develop a new vine density decomposition that accommodates such variables and employs an adequately randomized version of the Rosenblatt transform. This novel approach allows for more accurate modelling of multivariate zero-inflated climate data. Compared with state-of-the-art correction methods, VBC is generally the best-performing correction and the most accurate method for correcting zero-inflated events.

翻译：气候模型大集合是分析和量化自然气候变率、为罕见极端事件提供稳健信息的重要研究工具。由于对物理过程理解不完善，模型对现实的模拟表征容易存在偏差。本文旨在以3小时时间分辨率校正CRCM5大集合中五个气候变量在中欧地区的偏差。在此高时间分辨率下，降水和辐射两个变量表现出高度的零膨胀特征。我们提出了一种新颖的偏差校正方法VBC（藤Copula偏差校正），该方法通过建模并转移数据中零膨胀边缘的多元依赖结构，将其从易出错的模型域转换到参考域。VBC利用藤Copula估计模型分布与参考分布，并通过（逆）Rosenblatt变换校正模型分布。为处理变量的零膨胀特性，我们开发了一种新的藤密度分解方法，该方法兼容此类变量并采用适当随机化的Rosenblatt变换。这一创新方法能够更精确地建模多元零膨胀气候数据。与最先进的校正方法相比，VBC通常是性能最佳的校正方法，也是校正零膨胀事件最准确的方法。