The inherent variability of the solar resource presents a unique challenge for Concentrating Solar Power (CSP) systems. Incident solar irradiance can fluctuate widely over a short time scale, but plant performance must be assessed for long time periods. In this paper, the concept of Plausible Meteorological Year (PMY) along with the Multiscale Stochastic (MUS) methodology for its synthetic generation is presented. A PMY is herein defined as a high-frequency yearly series of Global Horizontal solar Irradiance (GHI), Direct Normal solar Irradiance (DNI) and other relevant meteorological variables (temperature, relative humidity, and wind speed, among others), which are statistically consistent with the estimated variability of the monthly and annual values of DNI and other variables. An efficient and robust scheme for the generation of a random number of PMYs for a specific location through the combination of various well-established methods is presented and explained. Analyzed data show that, differently from the GHI monthly and annual series which showed to be normally distributed, the DNI series are not normal in all cases analyzed. The concept of PMYs and the associated methodology are presented as a possible solution for the sought goal of stochastic simulation of CSP plants considering the uncertainty and variability inherent to the solar resource. This approach is aimed at the adoption of probabilistic approaches to model variability and uncertainties in both electricity production and system cost to achieve sound estimates of the economic feasibility of commercial CSP plants. (C) 2015 Elsevier Ltd. All rights reserved.