<![CDATA[Rainfall is seasonal, fluctuating, and influenced by external factors, requiring a method capable of capturing these patterns while incorporating exogenous variables. In this study, the Seasonal Autoregressive Integrated Moving Average with Exogenous Variable (SARIMAX) model is applied and compared with the SARIMA model to assess the extent to which the inclusion of the exogenous IOD variable improves rainfall forecasting accuracy in Pariaman City. The data used consist of monthly secondary data for rainfall and the IOD from January 2010 to December 2024. The results indicate that the SARIMAX model outperformed the SARIMA model in forecasting performance. The best-performing model was SARIMAX(0,0,0)(1,1,1)12 with a MAPE value of 36.25% and an RMSE of 174.03. The forecasting results are expected to serve as a reference for formulating regional economic development policies and managing climate-sensitive sectors, thereby enhancing the economic resilience of Pariaman City to climate variability.]]>
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