A Comprehensive Analysis of Homogeneity, Trends, and Breakpoints in Temperature and Precipitation Time Series in Iran

The present study aimed to investigate the homogeneity and trend of changes in annual minimum temperature, maximum temperature, and precipitation across 112 synoptic stations in Iran over a 30-year period (1991–2020). For homogeneity analysis, four tests (Pettitt, SNHT, Buishand, and von Neumann) were applied, while trend analysis was performed using the ordinary Mann–Kendall (MK) test and the modified Mann–Kendall test with pre whitening (MMK TFPW). The true slope of the trends was then estimated using Sen’s slope estimator. Subsequently, breakpoints (shift points) were identified, and stations were classified based on their degree of homogeneity. The results indicated that the SNHT test identified the highest percentage of homogeneous stations for minimum temperature (50%) and maximum temperature (41.96%), whereas for precipitation, Pettitt’s test showed the highest homogeneity (89.29%). Trend analysis using the MK method revealed a prevailing increasing trend in maximum temperature (86.92% of stations) and no significant trend in precipitation (91.96%). After applying the modified method (MMK TFPW) to remove the effect of autocorrelation, the percentage of stations with no significant trend increased markedly for all three variables (reaching 83.04%, 80.36%, and 100%, respectively). The results of the true trend slopes varied considerably among the stations. The years 1997 and 2008 were identified as the main breakpoints across all variables and tests. In the final classification, precipitation data were predominantly (74.11%) placed in the homogeneous class (A), while temperature data (especially maximum temperature) were largely classified as non homogeneous (class C, 77%). These findings suggest that temperature data are more sensitive than precipitation data to factors causing inhomogeneity. The variation in homogeneity results and breakpoints among different stations highlights the strong influence of local conditions and spatial heterogeneity on climatic time series and underscores the necessity of considering these factors in future studies and climate change modeling.