Accurate weather and climate prediction are essential for early warning systems that improve response strategies to climate-related events. This study explores the use of association rule mining (ARM) techniques to analyze large-scale meteorological datasets. We focus on the weather patterns of Tallinn and Tartu, investigating variables such as wind speed, temperature, precipitation, and humidity, and their influence on weather intensity. A distributed ARM approach is employed using the Apollo framework, which utilizes serverless functions to enhance scalability and performance. Results show Apollo outperforms traditional systems like Apache Spark by approximately 15% in terms of processing speed, while extracting a greater number of meaningful rules. Time-series analysis was also applied to investigate temporal weather trends. Our findings highlight the potential of this approach for enhancing weather prediction systems and offer a foundation for future research in this area.