## 4 Discussion  
SBP products provide new alternatives for station observations; however, uncertainties are associated with both gauge observations and SBP estimations. Unfortunately, these uncertainties are difficult to quantify and may have influenced the above results. Gauge instruments may suffer from systematic biases caused by wind-induced evaporation loss and underestimation of trace values. These biases are more prominent during the winter due to the lower precipitation totals and a higher prevalence of snow. Due to the lack of automatic gauge stations in high-elevation areas, conventional rain gauges measure qualitative precipitation amounts (rainfall + snowfall). Therefore, the evaluation of datasets during the summer is more reliable than during the winter, and the evaluation in this study mainly focuses on the summer monsoon season when the effect of evaporation loss in the observations is not significant due to the large precipitation totals. It is worth noting that the gauge-observed datasets used in this study are not wind corrected, due to the lack of wind speed data for the selected stations, but they were used to evaluate the IMERG-gauge datasets which are calibrated by the wind-loss corrected GPCC gauge analyses. This fact also weakened the certainty of the evaluation results in Section 3. Besides, among the DHM stations used in this study, data from 125 stations were merged to produce the GPCC gauge analysis, which was used to calibrate the GPM-IMERG product. Similarly, data from about 54 stations were also merged to produce the NOAA/CPC gauge-based analysis, which was used to correct the GSMaP product. The overlaps may lead to underestimation of the evaluation errors, which were not identified due to lack of information. Furthermore, previous studies revealed a quite large variability of precipitation in the high-elevation areas of Nepal with the importance of nocturnal precipitation [43]. In these regions, most of DHM gauge stations are located in valley bottoms [76; 77], where the nocturnal precipitation prevails. In such regions, the daily DHM gauge data do not capture the representative precipitation variability and may leave a gap in the performance quantification of SBP products. On the other hand, since the calibrated SBP products are corrected using the gauge-based analysis datasets, which present finer spatial patterns than the natural pattern, especially in mountainous regions. However, the calibrated SBP products may also smoothen the true spatial pattern in mountainous regions, and even skew some important signals contained in satellite-only products. Several previous studies also mentioned that topographic nature and regional climate are some dominant factors that influence the precipitation retrieval algorithm used in TRMM and GPM precipitation datasets [78]. The accuracy of the SBP precipitation data depends on various factors, such as regional effects and precipitation intensities. The scatter plot of precipitation rates between gauge observed, and SBP products were drawn using monthly precipitation data during the summer monsoon to quantify the performance of the SBP datasets for different precipitation intensities (Figure 9). GSMaP-Gauge, IMERG-C, IMERG-UC and GSMaP-MVK performed better for low precipitation rates (<10 mm/day) than for high precipitation rates (>10 mm/day), indicating that the SBP datasets have difficulty in estimating heavy precipitation. These SBP datasets also overestimated the amount of light rain and underestimated the amount of heavy rain. Such underestimation of heavy rain could be the reason for underestimated high-intensity related gauge observed extreme events (Figures 7b and 8) across the country. These discrepancies are primarily related to false precipitation in the form of light rain or solid precipitation and underestimation of heavy precipitation, respectively.