Understanding the daily weather types of any specific location is crucial for identifying its long-term climate patterns. In this study, we utilized the Wos classification method in conjunction with a comprehensive climatological approach to analyze key variables, including minimum, average, and maximum temperatures, as well as cloud cover and daily precipitation. Data from 1985 to 2021 were collected from 39 synoptic stations, which exhibited a well-distributed representation across the country and provided complete datasets. Weather types were identified using established coding techniques. The findings indicated that the predominant temperature types in the country are primarily categorized as hot and very hot, with sub-codes reflecting generally low to moderate cloud cover and negligible precipitation. Furthermore, the application of Ward's clustering method facilitated the identification of three distinct climatic groups. The geographical characteristics of each location, including factors such as altitude, latitude, proximity to the sea, and synoptic influences, play a significant role in the regional differentiation of these groups within the country. The outcomes of this research can be instrumental in developing weather calendars for various regions, with implications for numerous sectors including agriculture and tourism.