## 1 Introduction  
Accurate and reliable estimates of global climate patterns are directly associated with the regional variation in precipitation [1]. The changes in amount and pattern of precipitation could directly influence the water resources and agriculture of the concerned regions [2]. Therefore, understanding the spatiotemporal variation in precipitation on the regional scales is of great importance in climate monitoring and in hydro-climate studies [3]. Several researchers have reported the spatiotemporal variations of precipitation for different regions of the world [4; 5; 6; 7]. There is a growing agreement that long term changes in precipitation could alter the ecological and hydrological processes [8] and underpin our knowledge of global and regional climate change [9]. These accurate and reliable precipitation records underpin our knowledge of regional and global climate change, as well as their possible impacts on water resources [10; 11]. In general, gauge measurements are the basic and reliable way of precipitation data acquisition [12]. Unfortunately, scarce gauge records, irregular distribution, limited data access, and poor spatial coverage hinder their use in conducting hydro-meteorological studies and climate change assessments [13; 14]. In recent decades, with the advancement in remote sensing and geo-information technology, the gridded precipitation products (hereafter GPPs) has proven to be a reliable and cost-effective way of retrieving gridded precipitation data at various spatial and temporal scales across the globe [15]. These precipitation data either derived from satellite products or from the nationwide meteorological stations by using different interpolated algorithms and computational techniques by considering the physical characteristics (slope and elevation) of different regions. These multi-source data products are often applied as climatological input for hydro-climate simulation studies in data scarce extents to bridge the gap at regional scale [16] and there has been a considerable increase in the use of these products, owing to their easy accessibility, spatiotemporal coverage, and fine resolutions [17]. The evaluation of GPPs has also proved useful for different hydro-climate applications. The precipitation variability in different Gridded Data Products (GDPs) has been quantified for different regions across the globe [18; 19; 20]. Various studies were carried out in recent years to assess the performance of GDPs, revealing considerable differences between the products at the regional scale [21]. Furthermore, there are uncertainties that are associated with GPPs, because of the variability in spatial and temporal coverage, lack of in-situ observations, relocation of gauges, and data processing practices [22]. Thus, the reliability and accuracy of GPPs varies with time and regional climate [23]. Therefore, it is highly important to assess and evaluate the performance and capability of the GPPs at regional scales, especially the arid and semi-arid regions that are more sensitive to insignificant changes in climatic characteristics due to its fragile ecosystems [24]. Such regions are characterized by very complex hydrological systems that often exhibit extreme behaviors, such as extended droughs due to prolonged dry spell and floods due to high-intensity precipitation [25]. The predominantly arid and semi-arid climate and geographical location in the fast temperature rising region have made Pakistan one of the most vulnerable countries in the world to climate change [1]. Moreover, the natives of the country are mostly engaged in agriculture, a highly susceptible sector to climate change, with limited resources to adapt to changing circumstances [26]. Few studies were carried out in order to evaluate the performance of different GPPs against the reference data, which mainly focused on the basin level or higher altitude sites of the country and ignored the important segment of spatiotemporal precipitation variations in agricultural region of the country.