We investigate trends in extreme precipitation in Iran for 1951–2007 using the recently released APHRODITE daily rainfall time series. We find that seven different indices of extreme precipitation all show an upward trend through the study period. The seven different precipitation indices include annual precipitation total, number of days above a certain threshold, maximum precipitation received over a certain period of time, maximum one-day precipitation, and number of days with precipitation above the 90th percentile. A principal components analysis reveals one eigenvector explaining much of the variance in the seven indices and reveals that this component exhibits a strong upward trend for the whole of Iran. On a regional level, we find that the upward trend in extreme precipitation has a strong southwest-to-northeast gradient across the country for all the indices. We repeated all the analyses for 42 stations across the country to compare with the results from the gridded data; trends in extreme rainfall generated from the station data compare favorably with the results from the APHRODITE daily rainfall time series thereby reinforcing the robustness of our conclusions.

We assembled daily maximum and minimum temperature records for 31 stations throughout Iran over the period 1961-2010. As with many other areas of the world, we found that both the maximum and minimum temperatures were increasing overall with the minimum temperatures increasing twice as fast as the maximum temperatures. We gathered population data for the stations near the beginning and end of the temperature records and found in all seasons and for both the maximum and minimum temperatures the magnitude of population growth positively influenced the temperature trends. However, unlike so many other studies, we found the strongest population growth signal in the winter for the maximum temperatures. We found evidence that this winter-season population-temperature signal is related snow cover. Our results illustrate that any number of processes are involved in explaining trends in historical maximum and minimum temperature records.