There are numerous algorithmic classification methods that address the connections between different scales of the atmosphere, such as EOFs, clustering, neural nets. However, their relative strength encompasses the mean conditions. Extremes are poorly covered. A novel approach towards the identification of linkages between large-scale atmospheric fields and local extremes of meteorological parameters is presented. The principle is that objectively selected fields can be used to circumscribe a local meteorological parameter by way of regression. For each day, the regression coefficients form a pattern, source of a classification based on similarity. Several classes are generated which contain extreme atmospheric conditions and from which local meteorological parameters are computed, yielding an indirect way of determining local extremes from large-scale information. Not only can local meteorological parameters be subjected to this classification procedure. It can be extended to extreme indicators, e.g., threshold exceedances, yielding the relevant atmospheric fields to describe those indicators and grouping days with “favourable atmospheric conditions”. This approach can be extended by investigating networks of stations from a region and describing, e.g., the probability for threshold exceedances at a given percentage of the network. Not only can the method be used as a tool to objectively identify days in the current climate with extreme conditions. The method can be applied to climate projections, using the previously found parameter-specific combinations of atmospheric fields. From those fields, which constitute the modeled future climate, local time series can be generated which are then analyzed with respect to the frequency and magnitude of future extremes. The method has sensitivities (i) due to the degree to which there are connections between large-scale fields and local meteorological parameters (measured, e.g., by the correlation) and (ii) due to the varying quality of the different fields (geopotential, temperature, humidity et cetera) projected by the climate model.