Accurate precipitation prediction is crucial due to its potential to trigger various disasters, profoundly impacting communities worldwide. Reliable forecasts enable proactive measures to mitigate risks, such as floods and droughts, which regularly afflict populations globally. This precision is particularly significant for countries like India, where agriculture plays a pivotal role in the economy. Effective prediction relies on addressing challenges such as seasonal variability and transient patterns. This study employs a concise spatial analysis method to tackle these variations, integrating advanced deep learning techniques like the Multivariate Transient Convolutional Network (TCN) and Gated Recurrent Unit (GRU). Referred to as the e-TCN-GRU model, it achieved impressive metrics with a coefficient of determination (R2) and explained variance score (EVar) reaching 98.42% and 98.49%, respectively. Additionally, mean absolute error (MAE) and root mean square error (RMSE) were significantly lower compared to alternative models. These findings underscore the model's robustness and reliability in enhancing precipitation forecasting, crucial for environmental sustainability and disaster preparedness efforts globally.