The pre-monsoon period in India plays a critical role in shaping the country's agricultural calendar, especially for rainfed farming systems. In recent years, this transitional phase has been marked by increasing climate variability, making it difficult to predict rainfall patterns, temperature fluctuations, and the onset of the monsoon. This paper explores the dynamics of pre-monsoon variability, its implications for forecasting systems, and the resultant vulnerabilities faced by the agrarian sector. Using meteorological data, satellite imagery, and case studies from select Indian states, the study identifies key trends such as delayed rainfall, increased frequency of heatwaves, and inconsistencies in regional forecasts. The research highlights the challenges faced by farmers due to forecast inaccuracies and examines the socioeconomic risks associated with these disruptions, including crop planning failures and income instability. Further, the paper reviews the limitations of current forecasting models and suggests the integration of localized data, AI-based systems, and traditional knowledge to improve accuracy and accessibility. Finally, policy recommendations are offered to enhance resilience in agriculture through early warning systems, climate-smart practices, and better outreach. The findings underscore the urgent need for adaptive strategies in managing pre-monsoon uncertainty under changing climate conditions.