A semi-empirical downscaling approach is presented to estimate spatial and temporal statistical properties of local daily mean wind speed under global climate change. The present semi-empirical downscaling method consists of two elements. Since general circulation models (GCMs) are able to reproduce the features of the present atmospheric general circulation quite correctly, the first element represents the large-scale circulation of the atmosphere. The second element is a link between local wind speed and large-scale circulation pattern (CP). The linkage is expressed by a stochastic model conditioned on CP types. Parameters of the linkage model are estimated using observed data series; then this model is utilized with GCM-generated CP type data corresponding to a 2 x CO2 scenario. Under the climate of Nebraska the lognormal distribution is the best two-parameter distribution to describe daily mean wind speed. The space-time variability of wind speed is described by a transformed multivariate autoregressive (AR) process, and the linkage between local wind and large-scale circulation is expressed as a conditional AR process, i.e. the autoregressive parameters depend on the actual daily CP type. The basic tendency of change under 2 x CO2 climate is a considerable increase of wind speed from the beginning of summer to the end of winter and a somewhat smaller wind decrease in spring.