A probabilistic framework is presented for combining information from an ensemble of four general circulation models (GCMs), two greenhouse gas emission scenarios, two statistical downscaling techniques, two hydrological model structures, and two sets of hydrological model parameters. GCMs were weighted according to an index of reliability for downscaled effective rainfall, a key determinant of low flows in the River Thames. Hydrological model structures were weighted by performance at reproducing annual low-flow series. Weights were also assigned to sets of water resource model (CATCHMOD) parameters using the Nash-Sutcliffe efficiency criterion. Emission scenarios and downscaling methods were unweighted. A Monte Carlo approach was then used to explore components of uncertainty affecting projections for the River Thames by the 2080s. The resulting cumulative distribution functions (CDFs) of low flows were most sensitive to uncertainty in the climate change scenarios and downscaling of different GCMs. Uncertainties due to the hydrological model parameters and emission scenario increase with time but were less important. Abrupt changes in low-flow CDFs were attributed to uncertainties in statistically downscaled summer rainfall. This was linked to different behavior of atmospheric moisture among the chosen GCMs.