Specific electrical conductivity (SC) is a basic, effective indicator of water quality. The recent increase in SC data collected with high-frequency sensors has created a strong need for algorithms that can aid interpretation of these data. This study presents an algorithm that finds and quantifies SC temporal patterns and applies that algorithm to a data set from a forested catchment. During and after rain events, we show three patterns that emerge in SC time series: a solute flush, resulting in an initial increase in SC, followed by a dilution, followed by the SC’s recovery toward pre-rain conditions. We compared these SC patterns to precipitation amount and intensity, antecedent wetness, and seasonality. Our results indicate that the magnitude of the flush was driven primarily by precipitation intensity and total rainfall during a storm, and secondarily by antecedent moisture conditions. The magnitude of the dilution was driven mainly by precipitation amount. The rate of SC recovery was driven by precipitation amount and was correlated with the dilution. Overall, the algorithm successfully extracted event-driven characteristics in the SC time series, allowing the development of functional relationships with hydrologic drivers. Applying similar methodologies to more catchments in the future will help identify functional relationships at more sites and use these relationships to identify catchments most sensitive to future precipitation changes.