 The paper presents a bias correction method developed for climate impact modeling within the Intersectoral Impact Model Intercomparison Project, ASMIP. The method is designed to correct simulated historical data for systematic deviations from observations, using transfer functions generated to map the distribution of simulated data to that of observations. The bias corrected data is used as input for impact simulations in agriculture, water, biome, health, and infrastructure sectors at different levels of global warming. The method preserves the warming signal by correcting absolute changes in monthly temperature and relative changes in monthly values of precipitation and other variables needed for ISMIP. While the trend and long-term mean are well represented, limitations with regards to adjustment of variability persist, which may affect small-scale features or extremes. This article was authored by S. Hempel, K. Freeler, L. Worsowski, and others.