This study aims to demonstrate the use of association analysis for discovering the relationships between stream flow and climatic variables in the Kizilirmak River Basin in Turkey. Association analysis is a data mining technique that aims to discover rules in the form of A -> B that may occur in large datasets with frequency above a given threshold. A and B can be defined as events of a certain type, with the rule if A occurs then B occurs. In this study, A refers to climatic variable(s) (i.e., precipitation, temperature, wind speed, relative humidity) of certain magnitude, and B refers to the magnitude of stream flow. The interesting rules were quantified using support and confidence measures. Stream-flow data from three gauging stations in the Kizilirmak River Basin and climate data from three weather stations in the same basin were included in the analyses. All data were first segregated into three groups that were named as low, medium, and high. Low and high ranges of stream-flow data were further divided into three to increase our focus on extreme events. The analyses were conducted at the annual and seasonal timescales. The analyses indicated that the relationships between precipitation and temperature and stream flow are most prevalent but, relative humidity and wind speed are also important determinants of stream flow in the Kizilirmak River Basin.