## 1 Introduction
Other investigations in eastern Africa (Zimbabwe) show that ARC2 and RFEv2 estimate the precipitation gauge data better than other GPPs [17]. In addition, an evaluation conducted by [25] in equatorial and eastern Africa showed that IMERG performed better for daily scales, while CHIRPS outperformed other products at monthly and annual scales. Overall, the reliability of GPPs appears to be governed by a number of factors, including the study scale, location, time scale, and, most significantly, the availability of ground-based data used for calibration [17; 20]. Despite the significant efforts undertaken so far to evaluate GPPs, those products continue to require extensive validation against ground observations in order to assess their quality and quantify the appropriate level of confidence in their use for various hydrological applications [1]. Furthermore, ref. [26] highlights that the choice of GPP has a significant impact on runoff estimation and underlines the need for rigorous assessment with in situ observations to improve their confident application in water cycle research. As a result, temporal aspects and spatial distributions must be quantitatively analyzed. Nonetheless, the scale discrepancy problem persists when rain gauge data is used for validation. So far, the majority of existing GPP validation efforts in Africa have been conducted at large scales (country level or greater), with rain gauges separated by very large distances [8; 17; 27], and they were often performed on public datasets or GPCP-1 DD (Global Precipitation Climatology Project One-Degree Daily Precipitation) data [28]. Since numerous water management issues are dealt with at smaller scales, it is therefore of high interest to also evaluate the ability of GPPs to capture rainfall variations across short distances (5-10 km) for applications in medium to large watersheds (i.e., \(\sim\)10\({}^{3}\) km\({}^{2}\)) [29]. The aim of this study was, therefore, to evaluate, at different time scales (hourly to yearly), the reliability of six major GPPs (ARC2, CHIRPS, ERA5-Land (hereafter ERA5), IMRG v06 Final (hereafter IMERG), RFEv2, and PERSIANN-CCS (hereafter PERSIANN)) for water management applications in medium-size watersheds in Africa. More specifically, GPP data were evaluated against a network of rain gauges installed in the Ankavia watershed (1116 km\({}^{2}\)) in northeastern Madagascar. Water-related issues abound in Madagascar, strongly impacting economic development and environmental conservation [30; 31]. Indeed, northeastern Madagascar is characterized by heavy rainfall (1500 to 2500 mm/year), caused byoutheasterly exchanges that start in the Indian Ocean anticyclone and reach the highlands of the east [32]. This, along with deforestation from slash and burn, logging, and firewood harvesting, contributes to some of the world's greatest levels of erosion and catastrophic flooding [33; 34]. Furthermore, as a result of climate change, more powerful cyclones and increasing sea levels directly threaten coastal settlements and exacerbate floods and erosion in coastal areas [35]. In contrast, during the dry season, some rivers in the north tend to dry up, and alternative groundwater sources are not always available [32]. Previous research has shown that pressure on water resources in Madagascar is increasing [36; 37]. Hydrometeorological data are scarce and not always routinely collected, which impedes decision-making for integrated water resources management (IWRM), particularly at the basin scale [38; 39]. Hence the use of reliable GPP seems unavoidable for hydrological modeling, drought monitoring, and water resources management.