Rainfall is one of the most critical meteorological phenomena influencing agriculture, water resource management, flood control, and economic planning across the globe. Accurate prediction of rainfall is essential for governments, farmers, and disaster management agencies to take preventive and preparatory measures well in advance. Despite decades of research in meteorology and climatology, reliable rainfall prediction remains a complex challenge due to the highly non-linear and chaotic nature of atmospheric processes.

Traditional numerical weather prediction models and statistical approaches, while useful, are often limited in their ability to capture long-range temporal dependencies and handle noisy or incomplete data. The growing availability of large historical weather datasets and the rapid advancement of artificial intelligence have opened new avenues for data-driven approaches to weather forecasting.