Spatiotemporal analysis of long-term changes in air temperature is of prime importance for climate change research and the development of effective mitigation and adaptation strategies. Although there are considerable studies on air temperature change across the globe, most of them have been on linear trends and time series analysis of nonlinear trends have not received enough attention. Here, spatiotemporal patterns of monthly and annual mean (T-mean), maximum (T-max), and minimum (T-min) air temperature at 47 synoptic stations across climate zones in Iran for a 40-yr period (1978-2017) are analyzed. A polynomial fitting scheme (Polytrend) is used to both monthly and annual air temperature data to detect trends and classify them into linear and nonlinear (quadratic and cubic) categories. The significant (non-significant) trends in T-mean, T-max, and T-min across all climate zones are 41.1% (58.9%), 34.1% (65.9%), and 46% (54%), respectively. The highest magnitude of increasing trends is observed in the annual T-min (0.47 degrees C decade(-1)) and the lowest magnitude is for the annual T-max (0.4 degrees C decade(-1)). Across the country, increasing trends ((x) over bar) = 37.2%) have higher spatial coverage than the decreasing trends ((x) over bar) = 3.2%). Warming trends in T-mean, (65.3%) and T-min (73.1%) are mainly observed in humid climate zone while warming trends in T-max are in semi-arid (43.9%) and arid (34.1%) climates. Linear change with a positive trend is predominant in all T-mean, (56.7%), T-max (67.8%), and T-min( )(71.2%) and for both monthly and annual data. Further, the linear trends have the highest warming rate in annual T-min (0.83 degrees C decade(-1)) and T-mean, (0.46 degrees C decade(-1)) whereas the nonlinear trends have the highest warming rate in annual T-max (0.52 degrees C decade(-1)). The linear trend type is predominant across the country especially in humid climate zones whereas the nonlinear trends (quadratic and cubic) are mainly observed in the arid climate zones. This study high-lights nonlinear changes and spatiotemporal trends in air temperature in Iran and contributes to a growing body of climate change literature that is necessary for the development of effective mitigation and adaptation strategies in the Middle East.