Rainfall is a critical element of the climate and the analysis of it is essential for the agricultural management activities of a region. Therefore, being a monsoon-dominated state, the analysis of rainfall is important for the state of West Bengal. In this context, the simulation of rainfall is the most important aspect, but this attempt is missing in previous research works done by other researchers. This chapter was aimed to simulate the rainfall of West Bengal using the Naïve, ETS and TBATS model by utilizing the monthly rainfall dataset of 1901 to 2020. The whole methodology was implemented through a comprehensive eight-step procedure. After downloading the monthly rainfall data from India-WRIS; the dataset was standardized (0–1) first and the Naïve, ETS and TBATS models were implemented. After station-wise estimation of each model, the models were spatially evaluated and for each case, higher model values were marked for the Northern and Western sections and comparatively low model values were identified in the south-eastern sections of West Bengal. The accuracy assessment of each model was done at the last phase using Mean Error (ME), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Scaled Error (MASE) and Auto Correlation Error for Lag-1 (ACF1). The TBATS model outperformed the other models as it achieved low ME, RMSE, MAE and MASE and ACF1. The findings presented in this chapter are useful enough for understanding the climate crisis and the social response of the state.