## Data and Methods
### Methods  
In-situ data represents point-scale observations, which are not truly representative of the area averaged precipitation of the GPPs; thus, the interpretation of a direct comparison should consider such a problem. To overcome this, numerous studies have limited the comparisons to the pixels where an appropriate number of stations were present [12; 47], with a minimum of one to more than five stations in each pixel, according to the purpose of the study. In this study, we used minimum of one station to maximum of four in-situ stations in one degree-pixel. Furthermore, for GPPs, the nearest GPP precipitation value to the location of each station were retrieved for comparison (Figure 1b). The density of in-situ stations is not uniform and thus remapping the GPPs to a common grid size or area averaged precipitation for both in-situ and GPPs might introduce more noise into the analysis. Moreover, the in-situ data in the study region and usually in most of the developing countries is mostly recorded through traditional rain gauges, which does not accurately measure trace precipitation events (<0.25 mm/day) which were thoroughly checked for and treated. Four locations were selected for a regional scale comparison as shown in Figure 1b. These regions were selected based on variations in climate, elevation and land cover (Table 1), following the classification of [48] for the study region. The climate descriptions in Table 1 correspond to the climate zones 'Wet Semi-Arid,' 'Sub Humid,' 'Arid' and 'Extremely Arid' as described in Reference [48].The dominant features of the selected regions such as, the land cover, climate and precipitation pattern typically remain the same. The main seasons in the study region were classified as winter (December, January, February and March), pre--monsoon (April, May and June), monsoon (July, August and September) and autumn (October and November) following [35; 37]. For regional comparison, the precipitation products are compared at daily, monthly, annual and different seasons at interannual time scales. For a daily scale comparison, frequency versus intensity plots and Empirical Cumulative Distribution Function (ECDF) are used for comparison of in-situ and GPPs. Both in-situ and GPPs estimate precipitation with different procedures thus variability at daily scale is high and the information needed are obtained with using the above mentioned methods at daily scale thus scatterplot is not used for daily scale comparison. For frequency versus intensity plots, the regional precipitation observations were arranged in ascending order and grouped into bins of 1.85 mm per day following [49; 50]. The grouped precipitation observations were then normalized and plotted against station observations, where the frequency count (precipitating days) was placed on the y-axis and magnitude is shown on the x-axis. For daily scale ECDF, the cumulative probability derived for regional precipitation from in-situ and GPPs were compared using extreme precipitation thresholds of 2.5, 5 and 10 mm/day following [47]. To compare the similarity of precipitation between in-situ data and GPPs, a nonparametric Kolmogorov-Smirnov (K-S) significance test with 95% confidence level was applied, assuming that both in-situ data and GPPs have similar distributions. For monthly, annual, seasonal and interannual scale comparison at regional scales, the scatterplots were constructed against in-situ precipitation as independent and GPPs as dependent variables. The scatterplots express linear relationship between two or more than two variables. A best fit line (regression line) can easily infer the linear relationship between independent and dependent variable, whereas 1:1 fitting line can further simplify the nature of relationship as over- or underestimation of dependent variables to aid in the interpretation of results. The station scale comparison further elaborated the seasonal variation of correlation (Equation (1)) and RMSD (Equation (3)) at interannual scales.