With digital transformation and in the highly competitive retail market, it is important to understand customer needs and environmental changes. Moreover, obtain more profits through novel data mining technology is essential as well. Thus, the following questions should be addressed. Does climate influence the purchasing willingness of consumers? Do consumers buy different products based on the weather temperature? Few studies have used weather data and multilevel association rules to determine significant product combinations. In this study, real retail transaction records, temperature interval, and hierarchy class information were combined to develop a novel method and an improved association rule algorithm for exploring frequently purchased items under different weather temperatures. Twenty-six significant product combinations were discovered under particular temperatures. The results of this study can be used to enhance the purchasing willingness of consumers under a particular weather temperature and assist the retail industry to develop marketing strategies.