<p>This study is focused on the development, implementation and evaluation of an analog model designed to detect the occurrence of extreme precipitation events in southeastern South America based on the associated prevailing synoptic scale conditions. The approach is based on the use of synoptic patterns that allow the identification of different large-scale atmospheric conditions associated with local-scale extreme precipitation events. Synoptic patterns were identified after applying a principal component analysis to the 850&#xa0;hPa geopotential height for extreme precipitation days using ERA5 reanalysis over the period 1979–2013. Two detection criteria were applied to the daily standardized anomaly circulation fields to identify an extreme precipitation day. One is based on the spatial correlation coefficient between the daily standardized anomaly fields and the circulation patterns. The other is the consistency, over a selected region, of the sign of several meteorological variable anomalies and the sign of the anomalous patterns identified. The model captures the number of observed events and the observed interannual variations in the seasonal frequency of extreme precipitation events. This methodology also allowed understanding the mechanisms leading to higher or lower frequency of extreme precipitation events at the interannual timescales.</p>