To simulate multivariate daily time series (minimum and maximum temperatures, global radiation, wind speed, and precipitation intensity), we propose a weather state approach with a multivariate closed skew-normal generator, WACS-Gen, that is able to accurately reproduce the statistical properties of these five variables. Our weather generator construction takes advantage of two elements. We first extend the classical wet and dry days dichotomy used in most past weather generators to the definition of multiple weather states using clustering techniques. The transitions among weather states are modeled by a first-order Markov chain. Second, the vector of our five daily variables of interest is sampled, conditionally on these weather states, from a closed skew-normal distribution. This class of distribution allows us to handle nonsymmetric behaviors. Our method is applied to the 20 years of daily weather measurements from Colmar, France. This example illustrates the advantages of our approach, especially improving the simulation of radiation and wind distributions