## 1 Introduction  
Precipitation is one of the primary driving forces that shape the climatic scenario of any region. Irregular variation in precipitation impacts agricultural practices and productivity, water resource management, and ecosystem dynamics [1]. In hydrological studies, rainfall data are used to manage river flows, recharge groundwater and reservoirs, and assess droughts and floods, which are important for water resource sustainability. Therefore, accurate and consistent precipitation information is important for effective water resource management, agricultural practices, and ecosystems [2]. Moreover, for weather forecasting, monitoring, and the prediction of floods, droughts, and landslides, reliable precipitation estimates are more crucial [3]. However, acquiring the most reliable precipitation data with high spatial and temporal resolution is still challenging. Generally, rain gauge (RG) stations are considered the most reliable and accurate source of precipitation [4]. However, dense ground observations with consistent high-quality time series data are restricted to only a few regions of the world [5]; in the rest of the world, the low density, uneven distribution, limited spatial coverage, and temporal records hinder the usage of RG data for several applications, especially in mountainous regions where harsh climatic conditions impose difficulties for the installation and management of ground observation stations [6; 7]. The poor spatial coverage and irregular distribution of RG stations further constrain reliable representation of precipitation patterns across the study region [8]. Therefore, the acquisition of consistent precipitation with desirable spatiotemporal resolution is important for various applications [9]. Recently, various remote sensing and satellite-based global and regional precipitation products have become available, estimating precipitation at fine spatiotemporal resolution and applicable for multiple purposes. These global precipitation products are generally classified into four groups based on their data source and retrieval model, including RG precipitation-data-interpolated datasets, satellite-based precipitation datasets, merged precipitation datasets, and reanalysis precipitation datasets [8; 10]. The Climate Research Unit Time-Series dataset (CRU TS) is an example of an interpolated precipitation product, where the CRU of the University of East Anglia collects ground precipitation observations from various national and international sources and interpolates them at a global scale at 0.5\({}^{\circ}\) resolution [11]. Satellite-based precipitation datasets remotely estimate precipitation from visible/infrared/microwave radiations at a quasi-global scale. Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) is the most globally used satellite-based gridded precipitation dataset [12]. Reanalysis products are model-based products that consider atmospheric properties while merging historical observational data and satellite data to simulate precipitation at a fine resolution. The European Centre for Medium-Range Weather Forecasts (ECMWF) land surface model offers atmospheric variable-based (ERA5) and land surface variable-based (ERA5-Land) reanalysis precipitation datasets [13]. Integrated Multi-satellite Retrievals (IMERG) and Multi-Source Weighted-Ensemble Precipitation (MSWEP) are the two best examples of merged precipitation products. IMERG combines satellite-based participation products and mainly merges data from the Global Precipitation Measurement (GPM) mission and other satellites within the broader GPM constellation. Furthermore, it also considers ground observations to improve the reliability and accuracy of the precipitation estimates [14]. On the other hand, MSWEP combines a huge number of RGs' data across the world, multiple satellite-based precipitation estimates, and reanalysis products to produce high-resolution precipitation products [15].