Statistical Detection of Heatwaves Using Z-Score Method

Heatwaves are among the most dangerous climate extremes, causing serious impacts on human health, agriculture, and the environment. This study presents a simple and reproducible statistical method to detect heatwave events using daily maximum temperature data. A Z-score–based anomaly detection approach was applied to identify extreme temperature events relative to long-term climatic conditions. Rolling averages and rolling variance were used to analyse temperature trends and variability, while heatwaves were defined as periods of consecutive days with extreme positive temperature anomalies. The results indicate an increase in the frequency and persistence of heatwave events over time. The proposed methodology is transparent, computationally efficient, and suitable for student-level climate research and exploratory heatwave analysis.