Abstract. Water availability is highly influenced by variability of weather parameters. Minimum temperature and relative humidity are important parameters that have been sidelined in many water resources management projects. In this study, Autoregressive Integrated Moving Average (ARIMA) models were identified and diagnosed in order to forecast minimum temperature and relative humidity of the study area. The findings of the study show that minimum temperature was high during dry season, when relative humidity was low. Furthermore, the multiplicative seasonal models best fit minimum temperature and relative humidity represented as ARIMA (5, 1, 0)(2, 0, 0) 12 and ARIMA (1, 0, 0)(2, 0, 0) 12 respectively. While, a ten-year forecast derived from the models would be useful for effective planning and acquisition of water resources projects in the study area.