Temperature is one of the essential elements of forming a climate and plays a crucial role in the lives of flora, fauna and human activities. The extreme temperature is one of the thermal indexes in meteorological and climatological studies. The extreme temperature is divided into two types: the extreme warm and extreme cold. The extreme warm includes the temperatures much above the normal value and the extreme cold includes temperatures much below the normal value. Studying the extreme warm events due to their social and economical effects and their impact on human’s health has prominent importance. In order to regionalize the extreme warm of Iran, we used Sphezari dataset. The Sphezari base has been provided from the average temperature based on daily data from 663 synoptic and climatological stations from 1 January 1961 to 31 December 2004. The pixel of this dataset has been calculated in the form of 15 × 15 km2 and by kriging method. Therefore, the matrix dimensions of day to day temperature of Iran is in the form of 15992 × 7187 Sphezari dataset. In this dataset the rows (915992 days) represent the time and the columns (7187 pixel) represent the place. We have used normalized temperature departure index to identify the events of extreme warm events in this survey.The index has been introduced by Fujibi et al.(2007) .To obtain this index, the long term average temperature of calendar days must first be calculated.
The thermal amounts of 44 years are averaged to calculate the long term mean temperature of the given days. To avoid the existing noise in the daily mean temperature,the nine-day running average was applied three times in order to filter out day-to-day irregularities. After carrying out this phases , temperature departure ( ) of each of the 15992 days is investigated in the long term mean of the same day. Thus, it is necessary that the amount of the absolute temperature departure becomes standardized by the averages of . In this way, the amount of temperature departure in different times of a geographical point and different spatials in a particular time can be compared to each other. As an index of day-to-day variability, the variance of ΔT in the 31 days centered on each calendar day was calculated as Then the moving mean of nine days in three times will be conducted to dimnish the noise. Then normalized temperature departure (NTD) indexed with x* symbol was calculated. This index was calculated for 7187 pixels, each pixel for 15992 days. Then, the index of location x* was investigated over Iran and the percent area of Iran which had the amount of x*≥2 was determined. In this way, an index of 15992 × 2 was obtained, indicating the greatness highest temperatures of Iran for the period of 1 Jan 1961 to 31 Dec 2004. This matrix was arranged according to the mean of NTD and area amount. The first 264 days was selected as the sample. Whereas the temperature was in over of Iran, at least, 2 standard deviation more than its long term mean (x*≥2) and a large area was warmmer of Iran. The NTD of 7187 pixels in the selected 264 days was classified using the cluster analysis technique and agglomeration based on the entered method. Results of this research showed that according to the extreme warm events, Iran can be classified into five distinctive regions.The most important characteristics of the extreme warm events in Iran are as follow: Most of the extreme warm events of Iran have occurred in winter and autumn days. The maximum warm events of Iran has occurred in west and southwest of Iran, specially, in recent years. NTD is one degree above the other areas. The setting of this region with the maximum rate of the NTD index shows that the systems creating the extreme warm events was entered from west and southwest of Iran ; thus there are regions was influenced more and prior to the other regions. The highest spatial standard deviation belongs to these regions. It means that these regions have little spatial similarity from the viewpoint of the NTD index. It means that the extreme warm events creating systems donot attack this region equally. Some regions are influenced more and some less than others by these systems.
Maximum temporal standard deviation belongs to northern and western regions. This means that events of the extreme warm events happen in these regions in some months. Therefore the systems creating the extreme warm events in these regions are activated in part of the year. The least temporal standard deviation belongs to the northeastern region and the least spatial standard deviation belongs to south and southeast regions.