 The article discusses a bias correction method used in climate impact modeling to correct for systematic deviations between simulated historical data and observations. The method involves transfer functions that map the distribution of simulated historical data to that of observations, which are then applied to correct future projections. The bias corrected climate data is only provided over land areas, so it must preserve the warming signal in order to be consistent with global temperature information. The proposed methodology modifies the transfer function approach used in the water model intercomparison project, water MIP, and involves correcting monthly mean and daily variability about the monthly mean. 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.