Climate and rainfall are extremely non-linear and complicated phenomena, which require numerical modelling to simulate for accurate prediction. We obtained local historical rainfall data for 12 meteorological stations in the Vietnamese Mekong Delta (VMD) for the 45-year period 1978–2022, to predict annual rainfall trends. A statistical time series predicting technique was used based on the autoregressive integrated moving average (ARIMA) model. We utilized the seasonal ARIMA process of the form (p,1,q)(P,1,Q) for our study area. The best seasonal autoregressive integrated moving average (SARIMA) models were then selected based on the autocorrelation function (ACF) and partial autocorrelation function (PACF), the minimum values of Akaike Information Criterion (AIC) and the Schwarz Bayesian Information (SBC). The seasonal autoregressive integrated moving average model with external regressors (SARIMAX) was discovered, and a series of SARIMA models of various orders were estimated and diagnosed. To evaluate model fitting, we used the Nash–Sutcliffe coefficient (Nash) and the root-mean-square error (RMSE). The study has shown that the SARIMA (1, 1, 1)(2, 1, 1)11 and SARIMA (1, 1, 1)(2, 1, 1)12 model were appropriate for analyzing and forecasting future rainfall patterns at particular meteorological station in the VMD. The results showed the SARIMA model is more reliable and provides more accurate projections than other commonly used statistical methods, notably interval forecasts. We found that interpretable and reliable near-term location-specific rainfall predicts can be provided by the SARIMA-based statistical predicting model.