Large ensembles of climate models are indispensable for analyzing natural climate variability and estimating the occurrence of rare extreme events. Many hydrometeorological applications—such as compound event analysis, return period estimation, weather forecasting, downscaling, and bias correction—rely on an accurate representation of the multivariate distribution of climate variables. However, at high temporal resolutions, variables like precipitation often exhibit significant zero-inflation and heavy-tailed distributions. This inflation propagates through the entire multivariate dependence structure, complicating the relationships between zero-inflated and non-inflated variables. Inadequate modeling and correction of these dependencies can substantially degrade the reliability of hydrometeorological methodologes.In an earlier work \cite{copulas}, we developed a novel multivariate density decomposition for zero inflated variables based on vine copulas. This method has been integrated into multivariate Vine Copula Bias Correction for partially zero-inflated margins (VBC), with potential applications in other fields facing high-resolution climate data challenges. We resume the idea behind VBC and illustrate it’s advantages to other bias correction methods. This highlights the interpretability and the advantages of control and assessment of the results generated by VBC. The method is implemented in an R package available on GitHub \cite{models}