This technique is based on dynamical or statistical approaches commonly used in several disciplines, especially meteorology, climatology and remote sensing.
To overcome this problem, downscaling methods are developed to obtain local-scale weather and climate, particularly at the surface level, from regional-scale atmospheric variables that are provided by GCMs.
One form is dynamical downscaling, where output from the GCM is used to drive a regional, numerical model in higher spatial resolution, which therefore is able to simulate local conditions in greater detail.
[7] In 2007 the U.S. Bureau of Reclamation collaborated with U.S. Department of Energy's National Energy Technology Laboratory (DOE NETL), Santa Clara University (SCU), Lawrence Livermore National Laboratory (LLNL), and University of California's Institute for Research on Climate Change and Its Societal Impacts (IRCCSI) to apply a proven technique called "Bias Correction Spatial Disaggregation" BCSD;[8] see also "About on the Web site" to 112 contemporary global climate projections made available through the World Climate Research Program Couple Model Intercomparison Project, Phase 3 (WCRP CMIP3).
The effort resulted in development of 112 monthly temperature and precipitation projections over the continental U.S. at 1/8° (12 kilometres (7.5 mi)) spatial resolution during a 1950–2099 climate simulation period.