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Rainfall and Temperature remain natural climatic phenomena that are essential in the economic and agricultural productivity of developing nations. The predictions of these climatic phenomena were known to be challenging tasks due to their chaotic and complex natures. This research analyzed and modelled the seasonal autoregressive integrated moving average (SARIMA) of Annual Minimum, Maximum Temperature and Rainfall in the southwestern part of Nigeria using Ijebu Ode City as a case study with annual time series of the variables ranging from 1989- 2018. The study presented the methodology of the ARIMA model that integrates the seasonality of the series. Tentative numbers of SARIMA models were proposed for the variables built on the visualization of autocorrelation and partial autocorrelation functions of the series, a seasonal ARIMA (4,1,1)(1,1,1)12, ARIMA (1,1,1)(1,1,1)12, and ARIMA (2,1,1)(0,1,1)12 for maximum Temperature, minimum Temperature and rainfall respectively were adopted using the information selection criterion. The result of the forecast models shows that there is a tendency for an increasing pattern of annual rainfall and temperature over the forecast period from year 2019 to year 2028. The developed model can be of assistance in planning likely future strategies associated with the weather conditions of Ijebu Ode City and its immediate environment.