<p>Rainfall forecasting plays a vital role in water resource management, agriculture planning, and disaster preparedness. Holt Winters method relies solely on past values of a single variable. However, rainfall is influenced by multiple climatic factors, including temperature and humidity. In this study, we introduce two novel multivariate extensions of the Holt Winters method—Multivariate Additive Holt Winters (MAHW) and Multivariate Modified Holt Winters (MMOHW)—to enhance the accuracy of long range rainfall prediction. Using monthly data on rainfall, temperature, and humidity from Mizoram (1986–2023), the proposed models were evaluated against traditional univariate Holt Winters, Multiple Linear Regression (MLR), and Vector Auto Regression (VAR) methods by splitting the data into 80% training and 20% testing. The results demonstrate that the multivariate Holt Winters methods, particularly MMOHW, achieve significantly lower RMSE values (56.45 during training and 51.80 during testing) and lower MAE values in both training (25.60) and testing (21.33), with an NSE value of 0.67, indicating improved forecasting performance compared with the other models. Additionally, experiments involving the exclusion of one variable at a time confirmed the importance of incorporating all influencing factors for improved forecasting. These findings highlight the effectiveness of multivariate modeling in capturing complex environmental patterns and provide a robust framework for more reliable rainfall prediction.</p>