| Precipitation downscaling improves the coarse resolution and poor
representation of precipitation in global climate models, and
helps end users to assess the likely hydrological impacts of climate
change. This contribution integrates perspectives from meteorologists,
climatologists, statisticians and hydrologists, to identify generic
end user (in particular impact modeler) needs, and to discuss
downscaling capabilities and gaps. End users need a reliable
representation of precipitation intensities,
temporal and spatial variability, as well as physical consistency,
independent of region and season. In addition to presenting dynamical
downscaling, we review perfect prog statistical downscaling, model
output statistics and weather generators, focussing on recent
developments to improve the representation of space time
variability. Furthermore, evaluation techniques to assess downscaling
skill are presented. Downscaling adds considerable value to
projections from global climate models. Remaining gaps are
uncertainties arising from sparse data; representation of extreme
summer precipitation, sub-daily precipitation, and full precipitation
fields on fine scales; capturing changes in small-scale processes and
their feedback on large scales; and errors inherited from the driving
global climate model. |