Grosswetterlagen (GWL) are a well-known classification of synoptic weather types that connect regional weather impacts over Central Europe to the large-scale circulation over the wider European and N.E. Atlantic regions. GWL-REA is a new algorithmic method for classifying the GWL from reanalysis, climate and/or forecast model data. The method is based initially on pattern correlations using multiple target patterns for each GWL. Further optimisations ensure an equitable classification covering three key aspects of these synoptic types: consistency of the large-scale circulation, regional circulation constraints introduced by each GWL’s nomenclature and regional areal-mean precipitation. For the latter, a neural network is deployed to improve discrimination quality between respective anticyclonic and cyclonic GWL types. Temporal filtering is applied to remove noise on short timescales to make it easier for a person studying the output to assimilate the key aspects. A comprehensive verification of the GWL-REA outputs shows that the new method, when used with ERA5 reanalyses, is of a much higher quality than the original manual Hess-Brezowsky GWL series and to other comparable weather type classifications, in terms of both large-scale circulation consistency and in its applicability for regional weather impacts.