The analysis of hydroclimate extremes is gaining more attention due to the devastating effects of intense floods, droughts, etc. This study aims to analyze the stationary (S) and non-stationary (NS) behavior of the annual maximum temperatures (AMT) for two different climatic zones of Iran including the arid and excessively humid provinces of Kerman and West Azerbaijan, respectively. The research datasets included maximum temperature (from CRU TS) and soil moisture (from ERA5) on a monthly time scale (spanning 1901–2019 and 1979–2019). Trend, homogeneity, and stationarity tests were applied to define the basic characterization of the AMTs. The frequency analyses of the AMTs were carried out using Generalized Extreme Value (GEV) under two assumptions of S-GEV and NS-GEV. Moreover, the fitted distribution parameters were estimated using a maximum likelihood estimator (MLE). In addition to time-varying NS-GEV investigations, the soil moisture during summer (SM-June, July, and August) was also employed as the covariate to quantify the relationship between drought and AMTs in these climatic zones. The research findings revealed that the Akaike Information Criterion (AIC) in S-GEV and NS-GEV estimations decreased from 309 to 223 and 329 to 254 for arid and excessively humid climatic zones, respectively. Therefore, the NS-GEV frequency analyses has increasing effects on return levels of the AMTs than the S-GEV. In the following, the spatial NS-GEV investigations in all 12 and 15 stations of both provinces, showed that NS-GEV with SM as a covariate has better performance in excessively humid climatic zones.
