Prior to using climate data as input for sectoral impact models, statistical bias correction is commonly applied to correct climate model data for systematic deviations. Different approaches have been adopted for this purpose, however the most common are those based on the transfer functions, generated to map the distribution of the simulated historical data to that of the observations. Here, we present results of a novel bias correction method, developed for Inter-Sectoral Impact Model Intercomparison Project Phase 2b (ISIMIP2b) and applied to outputs of different GCMs generated within the HAPPI (Half A degree Additional warming, Projections, Prognosis and Impacts) project. We have employed various analysis measures including mean seasonal differences, ensemble variability, annual cycles, extreme indices as well as a global hydrological model to assess the performance of ISIMIP2b bias correction technique. The results indicate substantial improvements after the application of bias correction when compared against observational data. Moreover, the extreme indices as well as output of global hydrological model also reveal a marked improvement. At the same time, the ensemble spread of the original data is preserved after the application of bias correction. We find that the bias corrected HAPPI data can provide a reliable basis for sectoral climate impact projections.