This study presents a method for deriving high-resolution multimodel transient climate projections, that builds on the bias-corrected local scaling (BLS) method (Vidal and Wade, 2008a). This statistical downscaling method makes use of General Circulation Model (GCM) information on large-scale near-surface variables (precipitation and temperature) as well as a high-resolution meteorological dataset to build transient projections at the monthly time scale. The BLS method is based on disaggregation and bias-correction schemes initially developed for seasonal forecasting, and later used for climate change impact assessment. The downscaling framework previously applied to the UK with IPCC TAR GCMs consists of the following steps: (1) building appropriate precipitation time series from land areas covered by GCM sea or mixed cells; (2) quantile-quantile correction of GCM outputs inherent biases; and (3) disaggregation of bias-corrected outputs to a finer scale by using monthly spatial anomalies. This method has been found to compare well with other statistical and dynamical downscaling techniques (Vidal and Wade, 2008b). This study aims at presenting improvements implemented in the Bias-corrected Local Mapping (BLM) method and its application over the Garonne river basin located north of the Pyrenees mountain range in France, within the Imagine2030 project. The objective was to take account not only of mean monthly discrepancies between the regional and local scales already provided by the BLS method, but also of differences in the distributions due to the orography and to local-scale meteorological features. The third step is thus here replaced by a quantile-quantile transformation between interpolated GCM present-day fields and the 8 km resolution Safran reanalysis data over France (Vidal et al., 2010). The BLM method thus enables (1) to preserve the monthly temporal pattern of the transient simulations; (2) to take account of GCM ranked categories of large-scale spatial patterns; (3) to preserve the fine-scale local variations of statistical distributions; and (4) to consider multiple projections as equiprobable thanks to the quantile mapping steps. The BLM method is here applied over the Garonne river basin with an ensemble of IPCC AR4 GCMs run under the A2, A1B and B1 emissions scenarios. Results show an increase in temperature over the basin that is more pronounced in summer. The inter-model dispersion is much higher for precipitation, but all GCMs show a decrease in summer over the 21st century. Multimodel ensemble mean values suggest that temperature changes will be higher in the north-east of the basin, and that reductions in precipitation will affect more specifically the Pyrenees. Within the Imagine2030 project, the downscaled projections have been subsequently disaggregated temporally in order to drive hydrological models for assessing the impact on river flows.