## 1 Introduction  
Precipitation is a substantial constituent of the hydrological cycle that plays a crucial role in sustaining hydro-climatic balance and ecological processes. As precipitation varies in space and time, thus the accurate acquisition of precipitation data on desirable spatiotemporal resolutions is essential for the relevant applications, particularly in data-scarce catchments [1, 2]. However, acquiring the most reliable precipitation data on finer spatiotemporal scales is still challenging for researchers [3, 4]. The in-situ gauges can generally provide accurate and reliable measurements at gauge sites. Still, their scarce distribution,especially in mountainous regions, usually constrains the spatial representation of precipitation patterns [5; 6]. Moreover, hydro-meteorological studies are restricted by inhomogeneity, irregular distribution, and poor spatial coverage of ground-based observations. In recent years, satellite-based datasets have been introduced as an ancillary alternative source to detect spatio-temporal variation of precipitation with better spatio-temporal resolutions compared to conventional observations [7; 8; 9]. They are being used in different scientific research. Although, these datasets vary in design objectives (temporal homogeneity, instantaneous accuracy), source (analysis, reanalysis, gauge, radar satellite, or merged), spatial resolution and coverage (0.05\({}^{\circ}\) to 2.5\({}^{\circ}\) regional to worldwide), sequential span (-1 to 115 years), and computing (-half-hour to various years). These products are derived from a variety of sources. In general, such products are divided into the following four primary groups: (1) gauging-based datasets: which are created from observed station data using various interpolation methods and are being widely used, for instance, at the Global Precipitation Climatology Centre (GPCC); However, the spatial resolution of these products is low (0.5\({}^{\circ}\)), always argued particularly in data-scarce glaciated regions; (2) atmospheric model-based datasets, or numerical weather estimations, are derived from coupling both in-situ observations and satellite data, considering various atmospheric properties as input. The European Centre for Medium-Range Weather Forecasts (ECMWF) is an example of this product group; (3) satellite-based datasets, which mainly use infrared (IR), microwave (MV), or a combination of both IR and MV algorithms. Validation and assimilation with rain-gauge data are required primarily to increase the precision of these products. Furthermore, systematic biases in satellite rainfall data are common and can be largely pronounced in regions with low precipitation amounts or in snow and ice-covered areas. Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) is an example of this group. (4) Satellite-gauged datasets, such as Climate Hazards Group Infrared Precipitation with Stations (CHIRPS), merge both satellite and gauge datasets using diverse techniques [10; 11]. Hence, using the GPPs, either individually or in combination, offers a promising solution to the unavailability of climate data. These products have great worth in sparse and glaciated mountainous regions, especially in developing countries, where the distribution of conventional observations is heterogeneous [12]. At present, a variety of products are available at finer spatio-temporal resolutions and have been applied in multiple applications, for instance, hydrological modeling [13; 14; 15; 16], drought analysis [17], and agronomy [18]. However, these datasets still have some constraints, for instance, the topographical influences, retrieval algorithms, clouds, and inadequate sensibility of electromagnetic signals [19; 20; 21]. However, in a windy atmosphere and rugged terrain, detecting and measuring precipitation occurrences at high altitudes is a challenge for satellites as reflectivity from snow and ice reduces the effectiveness of passive sensing in mountainous regions [22].