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dry days, which are compensated by too much drizzle, a bias in the mean, and the inability to reproduce the observed high-precipitation events ( Boberg et al. 2009 ; Leander and Buishand 2007 ). For these reasons, hydrological models, which run offline, require bias-corrected and downscaled forcing data. Piani et al. (2010) have developed a statistical bias correction methodology for correcting climate model output to produce internally consistent fields that have the same statistical
dry days, which are compensated by too much drizzle, a bias in the mean, and the inability to reproduce the observed high-precipitation events ( Boberg et al. 2009 ; Leander and Buishand 2007 ). For these reasons, hydrological models, which run offline, require bias-corrected and downscaled forcing data. Piani et al. (2010) have developed a statistical bias correction methodology for correcting climate model output to produce internally consistent fields that have the same statistical
corresponding to the 99.999% lognormal probability precipitation rate for the relevant calendar month and grid box ( Weedon et al. 2010 ). As a result, some precipitation totals are less than the GPCCv4 totals in the WFD in a few locations and months. In a small number of grid boxes and some months precipitation rates are close to zero in the 1958–2001 ERA-40 data. The monthly bias correction then had the effect of increasing these rates such as to imply there was spurious background drizzle between more
corresponding to the 99.999% lognormal probability precipitation rate for the relevant calendar month and grid box ( Weedon et al. 2010 ). As a result, some precipitation totals are less than the GPCCv4 totals in the WFD in a few locations and months. In a small number of grid boxes and some months precipitation rates are close to zero in the 1958–2001 ERA-40 data. The monthly bias correction then had the effect of increasing these rates such as to imply there was spurious background drizzle between more
