 The paper presents a bias correction method developed for climate impact modeling within the Intersectoral Impact Model Intercomparison Project, ISIMIP. The goal is 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 method preserves the warming signal and applies corrections to monthly temperature, precipitation, and other variables needed for ISIMIP. While the trend and long-term mean are well represented, limitations with regards to adjusting variability persist, which may affect small-scale features or extremes. This article was authored by S. Hemple, K. Freeler, L. Warsawski, and others. We are article.tv, links in the description below.