Extended abstract
1- Introduction
During the 21st century, beliefs regarding extreme climatic occurrence has increased (IPCC, 2007). Nowadays, there is no newspaper that does not occasionally feature a story on climate change. Also, there are no specialized climate journals that do not include reports on world experts and scientists working on climate change and its differences in space. One of the key aspects of climate change is to understand the behavior of extreme events. Increases in extreme climate events, such as intense heavy rainfall days, have greater negative impacts on human society and natural environments than changes in climate means. IPCC (2007) concluded that changes in extremes of temperature are consistent with the observed warming of the climate. Over the past decade, a number of studies have attempted to identify observed and projected future changes in extreme precipitations. The results of the carried-out studies have shown that extreme precipitation has changed over some regions of the world. Also, the rate of change is not even. The aim of this study is the Analysis and recognition of variability in extreme precipitation indices in Iran during the last decades.
2- Theoretical bases
The WMO CCL/CLIVAR Joint Working Group on Climate Change Detection held another meeting in Geneva in November 1999, and recommended that a list of 10 simple and feasible indices be produced. This priority list of indices should be accompanied by methodologies and guidance on how to develop them for follow-up regional capacity building workshops in 2001. It was also emphasized that the development of indices should focus on indicators which were not highly correlated, but rather contain independent information. Indices should also be considered on a regional basis and compared within and between regions in addition to those for global analyses. Further consideration should also be given to developing indices to measure changes in climate variability on a variety of space and time scales (Frich et al., 2002). In this study, extreme precipitation indices were analyzed by daily precipitation data from 1437 synoptic, climatology and rain gauge stations. Daily precipitation data interpolated by Kriging methods for 15*15 km pixels. A matrix 15706*7187 obtained. In order to detect extreme precipitation indices, we used 11 indices for this parameter that were introduced by Expert Team on Climate Change Detection and Indices (ETCCDI), shown in Table 1.
Table1. List of extreme precipitation indices

ID Indicator name Definitions UNITS
RX1day Max 1-day precipitation amount Monthly maximum 1-day precipitation mm
Rx5day Max 5-day precipitation amount Monthly maximum consecutive 5-day precipitation mm
SDII Simple daily intensity index Annual total precipitation divided by the number of wet days (defined as PRCP>=1.0mm) in the year mm/day
R10 Number of heavy precipitation days Annual count of days when PRCP>=10mm Days
R20 Number of very heavy precipitation days Annual count of days when PRCP>=20mm Days
Rnn Number of days above nn mm Annual count of days when PRCP>=nn mm, nn is user defined threshold Days
CDD Consecutive dry days Maximum number of consecutive days with RR=1mm Days
R95p Very wet days Annual total PRCP when RR>95th percentile mm
R99p Extremely wet days Annual total PRCP when RR>99th percentile mm
PRCPTOT Annual total wet-day precipitation Annual total PRCP in wet days (RR>=1mm) mm
3- Discussion
The results show that extreme precipitation trend is positive in the southwest and western parts of Iran; while over a small part of the north, it was found to be negative. Indices did not follow any specific trend in the eastern part of Iran. Not only has extreme precipitation increased, but also the intensity has been risen during the study period. In other words, the precipitation was concentrated in some days of the year and the high value of precipitation occurred during days with extreme and super-extreme precipitation.
4- Conclusion
One of the signs of climate change occurrence is changes in the frequency of extreme precipitation indices; while for the mean, may be don’t observed any changes. Therefore, it is important that the behavior of extremes for the meteorology parameters have been assessed. In this study, 11 extreme indices of precipitation have been analyzed. Overall, the results showed that the anomaly in time of received precipitation and deviations from the mean and natural states result in disaster phenomena for an arid country like Iran. The increase of runoff and flash floods frequency in southwestern Iran are signs of change in extreme precipitation in this region.