Microclimate research has seen significant growth in recent years, particularly in areas such as outdoor thermal comfort, urban ecology, and urban heat mitigation. However, the short-term nature of many studies in this field presents challenges in ensuring that the collected data accurately represents local climate conditions. This paper introduces a novel method to enhance the quality and applicability of microclimate research by quantifying the representativeness of short-term meteorological data. Our approach employs the Kolmogorov-Smirnov (KS) statistic to compare daily meteorological data from nearby stations against long-term climate trends. Key findings demonstrate that this method effectively identifies representative data periods. This method allows researchers to evaluate the representativeness of each day's data according to their specific study objectives, whether focusing on typical or extreme weather conditions. By implementing this framework, researchers can: (a) Post-filter existing data to identify the most representative samples. (b) Quantify the climate representativeness of their findings, enhancing result interpretation and applicability. (c) More confidently generalize conclusions from short-term studies. The paper also provides simplified alternatives to the full method, making it accessible to a wider range of researchers. By adopting this approach, microclimate studies can achieve greater confidence in their data's representativeness, leading to more robust and generalizable conclusions. Our method addresses a key methodological challenge in microclimate research and provides a flexible data assessment framework. This framework enables researchers to systematically evaluate climate data representativeness, enhancing the reliability and applicability of their findings across various urban climate studies, from thermal comfort assessments to climate adaptation strategies.