## 1 Introduction  
Precipitation is a key factor in water and energy cycles and a significant number of studies have attempted to produce high resolution precipitation products for use in climate studies [1; 2; 3; 4]. The applications of precipitation products extend into hydrological and energy cycle characterization [5; 6] agricultural studies [7] and land-atmosphere interactions and feedback studies [8; 9]. These applications make use of precipitation data to improve precipitation forecasts and the related geophysical fluxes [10; 11], long term historical trends [12] and future projections based on historical records [13; 14]. In terms of natural hazards, the excess and shortage of precipitation has significant impacts on every aspect of society, including human casualties, agriculture, livestock, displacement and damage to civil and hydrologic structures [15; 16]. The primary land-based instrument for precipitation measurement is the rain gauge. The density of rain gauges is not uniform globally, with few rain gauges in remote regions and areas with limited access. In such cases, studies rely upon data from nearby rain gauges that is extended through statistical interpolation and extrapolation techniques. Although in-situ data have several limitations and uncertainties, they have been used for decades as a key source of precipitation information in decision making and planning and data assimilation [17]. In recent decades, remote sensing has become a promising tool for measuring precipitation. Radiances measured in the visible, infrared or microwave bands are transformed into precipitation with the help of statistically, quantitatively and physically based algorithms [18]. Remote sensing has proven a reliable and cost-effective way of retrieving precipitation at various scales. Among the most commonly used remotely sensed precipitation products include the Tropical Rainfall Measuring Mission (TRMM), the Multi-satellite Precipitation Analysis (TMPA), [19], the Climate Hazard group Infrared Precipitation (CHIRPS) [4], the Climate Prediction Center MORPHing product (CMORPH) [20], the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN) [21], the Multi-Source Weighted-Ensemble Precipitation (MSWEP) [22; 23] and the Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation (APHRODITE) [24]. The recent development in the precipitation retrieval is the launch of Global Precipitation Measurement (GPM) mission. GPM is a successor of TRMM, launched by NASA and JAXA with partnering agencies and countries during 2014. GPM consists of one core and eight constellation satellites, the core satellite is equipped with dual-frequency precipitation radar and a microwave radiometer and the constellation satellites have micro radiometers onboard [25; 26]. The precipitation products listed above have been developed for different purposes [27; 28]. Their applications are complicated and the selection of one product over another is always debatable. Therefore, it is important to evaluate such products against in-situ observations, which are considered to be the standard [27; 28]. The strengths and uncertainties of gridded satellite precipitation products (hereinafter to be referred to as GPPs) are thoroughly reported through comparisons with in-situ data from various locations. When in-situ data is used as reference data, GPPs perform well during warm and wet seasons over certain regions but their performance is less effective during cold seasons and in dry regions with less precipitation [29; 30]. The major uncertainties reported are an underestimation of precipitation and relatively high error metrics over oceans and across surfaces where the topography and land cover varies. The sources of uncertainty also vary and depend on the quality of in-situ data, which also contain deviations and uncertainties [27; 31].