In semi-arid areas, evapotranspiration that characterizes plant water use and water stress are needed to better manage water resources and agrosystem health. They both can be simulated by a dual source energy balance model that relies on hydro-meteorological variables and satellite data. Available hydro-meteorological observations may often be insufficient to account for the variability present in the study area. Our aim is to adapt a stochastic weather generator (SWG) driven by large-scale reanalysis data to semi-arid climates and to the sub-daily resolution. The SWG serves to perform consistent gap-filling and temporal extension of multiple hydro-meteorological variables. It is compared with two state-of-the-art bias correction methods applied to large-scale reanalysis data. The surrogate series that are either produced by the SWG and the bias correction methods with a cross-validation scheme or taken as the un-processed reanalysis data, are evaluated in terms of their ability to reproduce the statistical properties of the hydro-meteorological observations. They are also used to constrain a dual source energy balance model and compared in terms of estimated evapotranspiration and water stress.