Estimation of quantitative impacts of potential climate change on environment and various aspects of human existence requires high-resolution surface weather data. Since the direct output from general circulation models (GCMs) is unreliable at the local scale, alternative approaches ± most frequently based on statistical techniques ± should be used to downscale coarsely resolved GCM output patterns to ®ner spatial and/or temporal resolution. The downscaling techniques are brie¯y reviewed in the paper. Two of the approaches were followed in developing two versions of the stochastic weather generator (WG) called Met&Roll: (i) generation of synthetic weather series by generator with parameters derived from the local observed series and then modi®ed according to the climate change scenario that prescribes monthly increments of the individual variables; (ii) downscaling the GCM-simulated daily circulation pattern, using statistical linkage between the circulation patterns and the surface weather characteristics. Met&Roll-1 is a four-variate surface weather generator which employs a Markov chain approach to model precipitation occurrence and an autoregressive model to simulate the solar radiation and the diurnal extreme temperatures. The validation of the generator is performed by comparison of the stochastic structure of observed and synthetic series. Uncertainties in projecting the climate change scenario into the parameters of the WG are discussed. Met&Roll-2 is a generator which links the four surface weather variables with upper-air circulation patterns (CPs). CPs are characterized by principal components derived from 500 hPa geopotential ®eld. The series of CPs is either generated by autoregressive model or taken from the GCM output. The ®rst test of this generator is focused on the correlation between CPs and surface weather characteristics.