## 3 Materials and Methods
### Gridded Precipitation Products (GPPs)
#### 3.2.2 Satellite-Based GPPs  
The TRMM satellite provides the continuous precipitation data (1998-present) which covers the range from 50\({}^{\circ}\) N to 50\({}^{\circ}\) S over tropical to subtropical regions at 0.25\({}^{\circ}\) 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\({}^{\circ}\times\) 0.25\({}^{\circ}\) 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\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) 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\({}^{\circ}\) 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 \({}^{\circ}\)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\({}^{\circ}\) 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\({}^{\circ}\) N to 60\({}^{\circ}\) S and from 180\({}^{\circ}\) W to 180\({}^{\circ}\) E, respectively. For the present study, the hourly data converted into monthly time series during the period 2003-2017.