Global Climate Models (GCMs) used for climate studies and climate projections are typically run at spatial resolutions of the order of 150 to 200 km and are limited in their ability to resolve important sub-grid scale features such as convectionclouds and topography. As a result, GCM based projections may not be robust for local impact studies.
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. Two main forms of downscaling technique exist. 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. The other form is statistical downscaling, where a statistical relationship is established from observations between large scale variables, like atmospheric surface pressure, and a local variable, like the wind speed at a particular site. The relationship is then subsequently used on the GCM data to obtain the local variables from the GCM output.
Wilby and Wigley divided downscaling into four categories:regression methods, weather pattern-based approaches, stochastic weather generators, which are all statistical downscaling methods, and limited-area modeling (which corresponds to dynamical downscaling methods). Among these approaches regression methods are preferred because of its ease of implementation and low computation requirements.
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.
The Coordinated Regional Downscaling Experiment (CORDEX) was initiated in 2009 with the objective of providing a framework for the evaluation and comparison of downscaling model performance, as well as define a set of experiments to produce climate projections for use in impact and adaptation studies. CORDEX climate change experiments are driven by the WCRP CMIP5GCM outputs. CORDEX defined 14 downscaling regions or domains.
In technology terms, downscaling means to bring down something, usually referring to the resolution.
Hessami, M., Quarda, T.B.M.J., Gachon, P., St-Hailaire, A., Selva, F. and Bobee, B., "Evaluation of statistical downscaling method over several regions of eastern Canada", 57th Canadian water resources association annual congress, 2004.
Kim, J.W., Chang, J.T., Baker, N.L., Wilks, D.S., Gates, W.L., 1984. The statistical problem of climate inversion: determination of the relationship between local and large-scale climate. Monthly Weather Review 112, 2069-2077.
Maraun, D., Wetterhall, F., Ireson, A.M., Chandler, R.E., Kendon, E.J., Widmann, M., Brienen, S., Rust, H.W., Sauter, T., Themessl, M., Venema V.K.C., Chun, K.P., Goodess, C.M., Jones, R.G., Onof C., Vrac M. and Thiele-Eich, I., "Precipitation Downscaling under climate change. Recent developments to bridge the gap between dynamical models and the end user", Rev. Geophys. 48, RG3003, 2010.
Maraun, D. and Widmann, M., "Statistical Downscaling and Bias Correction for Climate Research", Cambridge University Press, Cambridge, 2018.
von Storch, H., Zorita, E., Cubasch, U., 1993. Downscaling of global climate change estimates to regional scales: an application to Iberian rainfall in wintertime. Journal of Climate 6, 1161-1171.
Wilby, R.L. and Wigley, T.M.L., (1997) Downscaling general circulation model output: a review of methods and limitations, Progress in Physical Geography, 21, 530-548.
Wilby, R.L., Dawson, C.W. and Barrow E.M., (2002) SDSM - a decision support tool for the assessment of regional climate change impacts, Environmental Modelling & Software, 17, 147- 159.
Wood, A. W., Leung, L. 5 R., Sridhar, V., and Lettenmaier, D. P.: Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs, Climatic Change, 62, 189-216, 2004.
Reclamation et al. "Bias Correction and Downscaled WCRP CMIP3 Climate and Hydrology Projections" <http://gdo-dcp.ucllnl.org/ downscaled_cmip3_projections/>
Xu, Z. and Z.-L. Yang, (2012) An Improved Dynamical Downscaling Method with GCM Bias Corrections and Its Validation with 30 Years of Climate Simulations. J. Climate, 25, 6271-6286.
Xu, Z. and Z.-L. Yang, (2015) A new dynamical downscaling approach with GCM bias corrections and spectral nudging. J. Geophys. Res. Atmospheres, doi:10.1002/2014JD022958
^Ribalaygua, J.; Torres, L.; Pórtoles, J.; Monjo, R.; Gaitan, E.; Pino, M.R. (2013). "Description and validation of a two-step analogue/regression downscaling method". Theoretical and Applied Climatology. 114 (1-2): 253-269. Bibcode:2013ThApC.114..253R. doi:10.1007/s00704-013-0836-x.
^Wilby, R.L.; Wigley, T.M.L. (1997). "Downscaling general circulation model output: a review of methods and limitations". Progress in Physical Geography. 21 (4): 530-548. doi:10.1177/030913339702100403.
^Wood, A. W.; Leung, L. R.; Sridhar, V.; Lettenmaier, D. P. (2004-01-01). "Hydrologic Implications of Dynamical and Statistical Approaches to Downscaling Climate Model Outputs". Climatic Change. 62 (1-3): 189-216. doi:10.1023/B:CLIM.0000013685.99609.9e. ISSN0165-0009.
^Gutowski Jr., William J.; Giorgi, Filippo; Timbal, Bertrand; Frigon, Anne; Jacob, Daniela; Kang, Hyun-Suk; Raghavan, Krishnan; Lee, Boram; Lennard, Christopher (2016-11-17). "WCRP COordinated Regional Downscaling EXperiment (CORDEX): a diagnostic MIP for CMIP6". Geoscientific Model Development. 9 (11): 4087-4095. doi:10.5194/gmd-9-4087-2016. ISSN1991-9603.
^Taylor, Karl E.; Stouffer, Ronald J.; Meehl, Gerald A. (2011-10-07). "An Overview of CMIP5 and the Experiment Design". Bulletin of the American Meteorological Society. 93 (4): 485-498. doi:10.1175/BAMS-D-11-00094.1. ISSN0003-0007.