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. This study analyzes the quality of the raw and post-processedseasonal forecasts of the European Centre for Medium-Range Weather Forecasts(ECMWF) System 4. The focus is given to Denmark, located in a region whereseasonal forecasting is of special difficulty. The extent to which there areimprovements after post-processing is investigated. We make use of twotechniques, namely linear scaling or delta change (LS) and quantile mapping (QM), todaily bias correct seasonal ensemble predictions of hydrologically relevantvariables such as precipitation, temperature and reference evapotranspiration( ET0 ). Qualities of importance in this study are the reduction ofbias and the improvement in accuracy and sharpness over ensemble climatology.Statistical consistency and its improvement is also examined. Raw forecastsexhibit biases in the mean that have a spatiotemporal variability morepronounced for precipitation and temperature. This variability is more stablefor ET0 with a consistent positive bias. Accuracy is higher thanensemble climatology for some months at the first month lead time only and,in general, ECMWF System 4 forecasts tend to be sharper. ET0 alsoexhibits an underdispersion issue, i.e., forecasts are narrower than theirtrue uncertainty level. After correction, reductions in the mean are seen.This, however, is not enough to ensure an overall higher level of skill interms of accuracy, although modest improvements are seen for temperature and ET0 , mainly at the first month lead time. QM is better suited toimprove statistical consistency of forecasts that exhibit dispersion issues,i.e., when forecasts are consistently overconfident. Furthermore, it alsoenhances the accuracy of the monthly number of dry days to a higher extentthan LS. Caution is advised when applying a multiplicative factor to biascorrect variables such as precipitation. It may overestimate the ability thatLS has in improving sharpness when a positive bias in the mean exists.

On the skill of raw and post-processed ensemble seasonal meteorological forecasts in Denmark