Accurate weather data extraction at small spatial domains is crucial for various applications, yet existing methods often lack precision. In this study, we propose a novel algorithm to enhance precision in weather data extraction for small spatial domains. Our approach utilizes an enhanced interpolation method, which involves identifying the domain boundary and generating equispaced interpolated points within the domain. These interpolated points are obtained from coarser grid climate data and are subsequently averaged to provide precise weather data within the smaller domain. The algorithm offers several key features, including the ability to accurately capture local weather variations, flexibility in defining domain boundaries, and efficiency in processing large datasets. Furthermore, it allows for seamless integration with existing weather data systems and can be easily customized to suit specific application requirements. Overall, proposed generalized algorithm provides a robust and efficient solution for enhancing precision in weather data extraction, making it invaluable for various fields such as agriculture, urban planning, and environmental monitoring.