## 2 Recent Works  
The gauges potentially provide _in-situ_, high time-resolution precipitation measurements at an area of a few square meters and also they have error due to wind, solid precipitation, _etc._ while the TMI and PR instruments essentially offer instantaneous area-averaged measurements with relatively poor time sampling. The measurement errors of both the satellite retrievals and the gauges are not fully understood [16; 17]. The comparison of satellite estimates with rain-gauge measurements is often frustrating, because satellites can at best attempt to measure rain amounts over areas many kilometers in size around a gauge, whereas rain-gauges can only record what falls in an area some tens of centimeters in diameter [18]. However, recently released IMERG is going to reduce this sampling issue such that GPM constellation satellites will have improved spatial and temporal resolutions (_i.e._, 30 min).  
Many attempts have been made to develop and improve global and regional gridded precipitation gauge data sets in recent years [19; 20; 21]. However, due to the lack of observational precipitation datasets over land areas for parts of Asia, [PERSON] _et al_. [22; 23] generated a high resolution rain-gauge based daily precipitation grid dataset for the East, Middle East and Russia.  
[PERSON] _et al_. [24] assessed the performance of five satellite-based rainfall outputs by comparison against gauge analysis: TRMM (3B42-V7, 3B42-RT) of US National Weather Service (NWS), Climate Prediction Center (CPC), Morphing Techniques (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and those of Naval Research Laboratory (NRL). They found that all satellite products performed better in wet climate regions and wet seasons, but showed limited skills in estimating precipitation over central Asian arid and semi-arid regions.  
[PERSON] _et al_. [25] constructed daily precipitation surfaces for Australia from the post real-time TRMM 3B42 rainfall product. This product has been used in stream flow and flood modeling studies where real-time gauge data is sparse [26; 27]. [PERSON] _et al_. [28] indicated that there is high correlation between TRMM rainfall data and the Australian Bureau of Meteorology monthly grid values data. Regarding the correlation between datasets as a function of climate, the steppe and temperate climate zones show a higher correlation than the tropical and desert zones. The quality of the analysis depends on the density of gauge reports (sparse data imply larger errors), the complexity of the local terrain, _i.e._, gauges in mountain areas are generally sited in valleys and, therefore, underreport the true areal average rainfall [29].  
A strong increase in tropical land precipitation during the past decade is found in ERA-Interim and Global Precipitation Climatology Project 2. While large improvements have been made in terms of temporal homogeneity compared with ERA-40, especially with regard to precipitation, the homogeneity issue still needs to be carefully addressed when interpreting surface flux data from reanalysis or other sources [30].  
The performance of GPM satellite-based precipitation has not been studied over arid and semi-arid areas in Iran. To address this, in our research, we compare precipitation estimates of GPM-IMERG-final run data sets with meteorological synoptic stations' data over Iran to evaluate the GPM-based precipitation outputs.