Atmospheric variables simulated from climate models often present biases relative to the same variables calculated by reanalysis in the past (SAFRAN reanalysis for example). In order to use these models to assess the impact of climate change on processes of interest, it is necessary to correct these biases. Currently, the bias correction methods used operationally correct one-dimensional time series and are therefore applied separately, physical variable by physical variable and site by site. Multivariate bias correction methods have been developed to better take into account dependencies between variables and in space. In this work, we propose a comparison between two multivariate bias correction methods (R2D2 and dOTC) and a univariate correction (CDF-t) through several highly multivariate impact models (phenological stage, reference evapo-transpiration, soil water content, forest weather index) integrating the climatic signal throughout a season. The data, the impact models and the statistical methods are first presented. The experimental design is then described. Extensive results are illustrated but not commented.