3.2.1. Gauge-Based GPPs

The Global Precipitation Climatology Centre (GPCC) obtained the primary data from meteorological agencies at National level (NMAs), Food and Agriculture Organization (FAO), Climate Research Unit, University of East Angelia (CRU), and Global historical climatology network (GHCN) at the National Centers for Environmental Information [48]. The product covers the period from 1891 to 2018. In this research, the monthly product of 0.5◦ spatial resolution is used from the period 1979–2017. The product from University of Delaware, USA (UDel) acquired the primary data from GHCN2, daily GHCN from National Centers for Environmental Information, National Center Atmospheric Research (NCAR), Project Greenland of automatic meteorological stations, Data archives of Nicholson for African continent, records of daily summary at the global level [49]. The product covers the period from 1900 to 2017. The monthly precipitation data of 0.5◦ spatial resolution from the period 1979–2017 is utilized in the study. The monthly product Climate Research Unit (CRU) acquired the primary data from the World Meteorological Organization (WMO), Food and Agriculture Organization (FAO), and National meteorological departments. The CRU product covers the period from 1901 to 2019. The monthly precipitation data of 0.5◦ spatial resolution from the period 1979–2017 is used in the present study [50].

The daily Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) product that was obtained the primary source data from multiple Asian agencies including Global Telecommunication System (GTS), National weather agencies, International Centre for Integrated Mountain Development (ICIMOD), International Water Management Institute (IWMI) and other national and international projects on climate [51]. The product covers the period from 1961 to 2015. The daily precipitation product of 0.5◦ spatial resolution was accumulated into monthly time scale from the period 1979–2015 in the present study. The CPC is the first product of Unified Precipitation Project at the National Oceanic and Atmospheric Administration (NOAA). The product acquired the primary data from National weather agencies, quality controlled station data from GTS, and Cooperative Observer Network [52]. The CPC product covers the period from 1948 to 2018. The daily precipitation product of 0.5◦ spatial resolution was accumulated into monthly time scale from the period 1979–2017 in the present study.

### 3.2.2. Satellite-Based GPPs

The TRMM satellite provides the continuous precipitation data (1998–present) which covers the range from 50◦ N to 50◦ S over tropical to subtropical regions at 0.25◦ spatial resolution [53]. The TRMM Multi-satellite Precipitation Analysis (TMPA) used three basic instruments to record the data, including Visible Infrared Scanner, Microwave Image and Radar Precipitation [30]. The TMPA provides real time and post real time products. The post real time version 7 products have adjusted with gauge data and proved higher accuracy than TMPA real time product [54]. The present study utilized the higher accurate version 7 products with monthly temporal and 0.25◦ × 0.25◦ spatial resolution during the study period 2003–2017.

The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) provides longer gridded data (1983–present) with spatial resolution of 0.25◦ × 0.25◦ at the daily timescale. The PERSIANN-CDR estimates the rainfall rate from geostationary satellites by using infrared brightness temperature [55]. The stage IV radar data from the National Centers for Environmental Prediction (NCEP) is used to train the Artificial Neural Network (ANN) model. The high resolution precipitation estimates then attuned by Global Precipitation Climatology project (GPCP) for bias correction [56]. The daily data aggregated into monthly time scale for evaluation against reference data for the study period 2003–2017.

The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS) provides the gridded precipitation data (2003–present) at spatial resolution of (0.04◦ and 30 min.) by using Infrared brightness temperature derived from geostationary satellites and continuously updating its parameter using Passive Microwave (PMW) measurements from low earth orbit satellites. The regression and histogram matching are used to draw fit curve plot between temperature brightness of pixel and rainfall rate to achieve rainfall mapping of each classified cloud cluster [57]. The daily data aggregated into monthly time scale for evaluation against reference data for the current study period 2003–2017. The CHIRPS precipitation dataset is quasi land product belongs to Climate Hazard Group with a spatial resolution of 0.05 ◦s and temporal daily scale derived from TRMM satellite, and several observed products such as NOAA, CPC, National Climatic Data Center (NCDC) and Climate Forecast System version 2 (CFSv2). The product algorithm is based on the concept of cold cloud duration (CCD), which is the duration of time of pixel covered by IR brightness temperature. Precipitation estimates by using CCDs procedure by incorporating TMPA3B42 product and merged with observed measurements using the Inverse Distance Weighting (IDW) method to produce final product [58]. For the current study, the daily data aggregated into monthly time scale during the study period 2003–2017. The recent GSMap project is launched by Japan Aerospace Exploration Agency (JAXA) to monitor precipitation at higher spatial resolution of 0.1◦ and one-hour temporal scale. The product input is based on multiple polar orbiting satellites with adjusted accuracy of Kalman smoothing approach [59]. The product covers the range of latitude and longitude from 60◦ N to 60◦ S and from 180◦ W to 180◦ E, respectively. For the present study, the hourly data converted into monthly time series during the period 2003–2017.


**Table 1.** Information of gauge based (GB) and satellite based gridded data products (SB GPPs) used in the study.
