## 1 Introduction  
Precipitation is one of the major driving components of the hydrologic cycle [1]. Therefore, accurate precipitation estimates are essential for many professionals including meteorologists, hydrologists, ecologists, agriculturists, disaster management personals, energy planners, etc. for decision making and planning in various hydrologic related activities. Generally, precipitation estimates are obtained from rain gauges (RGs), meteorological radars, satellites, and re-analysis products. Technological and economic constraints limit the usage of high-tech weather radars in developing countries such as Thailand for hydrologic related applications [2]. Re-analysis data are produced through the combination of past short-range weather forecasts with observations through data assimilation. These re-analysis products can suffer from significant biases in the tropical regions [3; 4; 5]. Thus, ground-measured rainfall measurements are treated as the best for their accuracy [6]. Observed rainfall data are highly used in many hydrologic related applications. Interpolation of RG data is used to derive the gauge-based gridded precipitation products (GbGPPs). Noteworthy, dense meteorological networks are required to capture the inherent higher spatial variability of rainfall. Nevertheless, a dense meteorological network is not always possible in most regions. The Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) [7], Global Precipitation Climatology Center (GPCC) [8], Climate Research Unit (CRU) [9], Climate Prediction Center Global Precipitation (CPC-GP) [10] are some of the most commonly used GbGPPs in the context of water resources and climatology. The advancements of satellites and remote sensing (RS) technologies have enabled the observation of many climatic variables (i.e., temperature, rainfall, and humidity), atmospheric parameters (i.e., aerosol content, the concentration of greenhouse gases, etc. ), and terrestrial water cycles (i.e., terrestrial water storages). These satellite-based precipitation products (SbPPs) can be obtained without many disruptions, unlike the observed meteorological data. In addition, they are readily available for most of the regions of the world. More importantly, SbPPscan be extracted free of charge, which is an added advantage. Satellite technology was utilized to measure rainfall from as far as the 1970s and has achieved tremendous progress over the past few decades [11]. The Tropical Rainfall Measuring Mission (TRMM) is considered the first satellite mission, which was aimed at investigating the latent heat cycles of the tropical regions [12]. It was a joint mission between the North Atlantic Space Agency (NASA) and the Japanese Aerospace Exploration Agency (JAXA). Precipitation Estimation based on Artificial Neural Networks techniques (PERSIANN) [13], Climate Prediction Center (CPC) MORPHing technique (CMORPH) [14], Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) products [15], Integrated MultisatellitE Retrievals for Global Precipitation Measurements (IMERG) [16], Global Satellite Mapping of Precipitation (GSMap) [17], Naval Research Laboratory developed blended-satellite precipitation technique (NRL-blend) [18], Multisource Weighted-Ensemble Precipitation (MSWEP) [19], and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) [20] are some of the SbPPs, which cover most of the regions of the world. In addition, to the aforementioned products, some SbPPs are limited to certain regions. Rainfall Estimate (RFE) and Tropical Applications of Meteorology using SATellite, (TAMSAT v.2 and v.3) (TAMSAT), African Climatology Project (APC) v.2 [21] which cover the African continent and the Combined Scheme Approach (CoSch) [22], which cover the South American continent are few examples for region specific SbPPs.