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. Many meteorological forcing datasets include bias-corrected surfacedownwelling longwave and shortwave radiation (rlds and rsds). Methods usedfor such bias corrections range from multi-year monthly mean value scaling toquantile mapping at the daily timescale. An additional downscaling isnecessary if the data to be corrected have a higher spatial resolution thanthe observational data used to determine the biases. This was the case whenEartH2Observe (Calton et al., 2016) rlds and rsds were bias-correctedusing more coarsely resolved Surface Radiation Budget(Stackhouse Jr. et al., 2011) data for the production of the meteorologicalforcing dataset EWEMBI (Lange, 2016). This article systematicallycompares various parametric quantile mapping methods designed specificallyfor this purpose, including those used for the production of EWEMBI rlds andrsds. The methods vary in the timescale at which they operate, in their wayof accounting for physical upper radiation limits, and in their approach tobridging the spatial resolution gap between E2OBS and SRB. It is shown howtemporal and spatial variability deflation related to bilinear interpolationand other deterministic downscaling approaches can be overcome by downscalingthe target statistics of quantile mapping from the SRB to the E2OBS grid suchthat the sub-SRB-grid-scale spatial variability present in the original E2OBSdata is retained. Cross validations at thedaily and monthly timescales reveal that it is worthwhile to take empiricalestimates of physical upper limits into account when adjusting eitherradiation component and that, overall, bias correction at the daily timescaleis more effective than bias correction at the monthly timescale if samplingerrors are taken into account.

Bias correction of surface downwelling longwave and shortwave radiation for the EWEMBI dataset