## 2 Study Area, Data, and Methodology
### Methodology  
#### 2.3.1 Ground-Based Data Preparation  
Table 2 provides information on the data gaps percentage. Since the computation of precipitation indices would be affected due to the data gaps, though small, we first attempted to fill these gaps using the best available reanalysis or/and interpolated products. We estimated precipitation values using a point-to-pixel [47] approach for the data gap days. Asian Precipitation--Highly-Resolved Observational Data Integration Toward Evaluation (APHRODITE) (available for 2001-2015), with 0.25\({}^{\circ}\) spatial resolution, was used for the data from 2001 to 2015, and NOAA Climate Prediction Center (CPC) with 0.5\({}^{\circ}\) spatial resolution was used for the remaining period from 2016 to 2020. Quality-controlled gauge data are used to generate interpolation-based gridded data for both products, i.e., APHRODITE [85] and CPC [86]. A refined APHRODITE algorithm (under the APHRODITE-2 project from June 2016 to March 2019) was used for the precipitation data from 1998 to 2015. If the data gaps were substantially large, these data fillings using low-resolution products (i.e., 0.25\({}^{\circ}\) and 0.5\({}^{\circ}\)) would have a greater influence. Here, the objective of this paper is to analyze the spatial details of several precipitation indices based on four IMERG datasets rather than GP stations. However, to have better confidence in the applicability of IMERG datasets, we attempt to compare them with GP stations at key locations. Therefore, though the data-filling process would bring some uncertainty, the effects are not considered in this study.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**SN** & **Datasets** & **Description** \\ \hline
1 & IMERG\_Cal & Multi-satellite precipitation estimates with gauge calibration \\
2 & IMERG\_Uncal & Multi-satellite precipitation estimates without gauge calibration \\
4 & IMERG\_HQ & Merged microwave-only precipitation estimates \\
4 & IMERG\_IR & Infrared-only precipitation estimates \\ \hline \hline \end{tabular}
\end{table}
Table 1: Description of types of IMERG datasets.  
Nevertheless, we compared precipitation data of two gridded products (i.e., APHRODITE and CPC) during non-data gaps. We observed that these products have fewer biases and apply to filling data gaps. It is to be noted that they, however, might not exactly replicate the ground conditions during the data gap days on a day-to-day basis. Importantly, these gridded datasets generally conserve spatial characteristics on a seasonal and annual scale. Figure 1 shows the spatial distribution of the network of selected precipitation stations. The mean annual precipitation (MAP) from 2001 to 2020 ranges from 658.8 mm at Larke Samdo (St. No. 806) to Bhadaure Deurali (St. No. 813), representing a more-or-less country's precipitation variability [79,80]. The arithmetic average of 25 selected MAPs is 1799.5 mm, equal to a country-averaged MAP [79,80] (Table 2). Based on normal climatic values, we can infer that the selected 25 stations represent key locations of the country and provide indicative information of country-averaged precipitation.