## 4 Discussion  
For the validation of the GPP data using ground-based rain gauges, the point-gridded approach performs similarly or better than the point-to-grid approach (Figure 3). Since the grid cell centroids rarely coincide with the rain gauge position, the point-to-grid method is subject to greater inaccuracy [60; 61]. This inaccuracy grows in proportion to the spatial resolution of the GPP [62]. In contrast, the point-gridded approach reduces this mismatch problem between gauge data and GPP data while preserving the resolution of the gridded data. Nevertheless, the point-gridded technique is rarely studied [62]. At the daily time scale, among the six GPPs, IMERG performed best at our study location (Figure 3). Its overall performance is reasonably good despite the small number of available rain gauges used for the Global Precipitation Climatology Centre (GPCC) in Africa, especially in Madagascar [53], resulting in biased precipitation data at rain gauge and catchment scale. Several previous studies have also underlined that IMERG outperforms other GPPs in tropical areas [20; 63; 64]. This has been attributed to the GP  
Figure 8: Probability distribution function of (**a**) rainfall duration, (**b**) rainfall depth, and (**c**) rainfall intensity at event time scale for ground-based rain gauges and IMERG data from September 2018 to August 2020. Microwave Imager (GMI) and the ability of the Ku/Ka-band Dual-frequency Precipitation Radar (DPR) to capture precipitation more effectively than the IR sensors and/or direct PM technologies used by other GPPs [53]. Specifically, the GMI instrument is a conically scanning, multi-channel microwave radiometer with 13 channels ranging in frequency from 10 GHz to 183 GHz [53]. The GMI employs a set of frequencies that have been refined over the last two decades to recover most ranges of precipitation, with the polarization difference of each channel serving as an indicator of optical thickness, water content, and precipitation systems [53]. Additionally, RFEv2, ARC2, and PERSIANN all show satisfactory performance (Figure 3). In contrast, CHIRPS and ERA5 perform poorly in the daily assessments. Some factors, such as gauge calibration and the sources of products play significant roles in the performance of GPPs [62]. In general, satellite-based precipitation products are currently more appropriate for meteorological applications than model-based products (e.g., ERA5) in tropical areas, especially over regions with extreme precipitation events. Nevertheless, reanalysis products such as ERA5 could have significant advantages in temperate regions or for research focusing on occurrence detectability at finer resolutions over high-latitude areas [49]. Therefore, the fact that some products (ARC2, IMERG, PERSIANN, RFEv2) are gauge calibrated and satellite-based likely explains their better performance compared to CHIRPS and ERA5. Table 3 summarizes the results from various studies that evaluated the performance of IMERG at a daily time scale in broadly similar climatic environments to the present study (humid tropical environment). Note that almost all of those studies used a point-to-grid or grid-to-grid approach; hence, the performance assessments are largely influenced by the density of the gauge network taken as a reference [63; 65; 66]. In terms of correlation (CC), the results from our study are among the highest reported so far, similar to [63]. RMSE and MAE values in the present study are within the range of those reported previously. FAR values are also within the range of previously reported values, whereas POD and CSI are somewhat lower in the present study compared to previous studies. Nevertheless, IMERG's fairly high POD, CSI, and low FAR suggest a good detection capability for daily rainfall, particularly in the range of 0-100 mm/day (Figure 6), even though some rainy days are still being missed. Precipitation events estimated by the GPP may not be detected by the gauges as it might rain at other locations within the grid-cell area.