This project aims at developing and implementing post-processing approaches for (re-)calibrating the MiKlip decadal prediction ensemble; addressing the typical problems encountered for decadal predictions, i.e. relatively small ensemble sizes and limited availability of hindcast-observation pairs. Starting with normally distributed variables, strategies will be specifically tailored to problems encounter for decadal predictions (model drift, climate trend). In a later stage, non-normally distributed quantities will be considered, as variables relevant to the end-user do not necessarily follow a Gaussian distribution (e.g. precipitation, humidity or wind gusts). Moreover calibration methods for probabilistic forecasts of dichotomous and countable events (e.g., droughts) will be also considered. An implementation into the central evaluation system (CES) allows all other MiKlip projects to (re-)calibrate the ensemble predictions and prepares the calibration for operational use.