## 1 Introduction
The results showed that satellite products could provide precipitation estimates that were close to station observations at all three temporal scales, potentially making them a valuable alternative to rain gauges [31]. An evaluation of light rainfall data obtained from satellite products at the daily scale in the Chinese mainland also demonstrated the successful detection of the spatial pattern of light rainfall, although the detection ability for small rainfall events (less than 5 mm/d) was limited. In areas characterized by more complex winter precipitation types, satellite products generally perform poorly, showing a relatively large deviation in the estimation of light rainfall [32]. Along with the development of remote sensing technology and the advancement of inversion algorithms, more and more precipitation products with a high spatiotemporal resolution have been released to the public. The integrated multi-satellite retrievals for the global precipitation measurement (GPM) product (hereafter referred to as IMRG) has received significant attention in recent years as it represents a new generation of global satellite precipitation products [33]. Numerous studies have evaluated the accuracy of IMRG data in recent years [17; 34; 35; 36]. IMRG has a higher spatiotemporal resolution and provides significantly more accurate precipitation estimates compared with products of the previous generation, and it also has broader application prospects in various research fields [17; 37; 38; 39; 35; 37]. When considering the estimation of extreme precipitation, the performance of IMRG is relatively consistent with that of its predecessors in terms of precipitation distribution, but the new technology has significant advantages in the estimation of heavy precipitation [37]. IMRG has also demonstrated a greatly improved ability to detect rainfall in the high altitude areas, such as southern region of the Tibetan Plateau [39]. The evaluations of satellite precipitation products primarily aim to reveal their error sources and improve the inversion algorithms. Although such observations at the monthly scale were used to correct IMERG and improve its ability to measure precipitation, inversion uncertainties at higher temporal resolutions are still present due to diverse land surfaces and climate conditions. The study of diurnal cycle also requires high temporal precipitation data, such as hourly scale, which is a fundamental cycle in the Earth system and related to both regional and large-scale thermodynamic processes [21; 40; 41]. Therefore, quality assessments are necessary before using IMERG-derived hourly data for hydrological and meteorological research. Currently, IMERG data are generally evaluated at the regional or continental scale [34; 42; 43; 44], but few studies have also accessed their accuracy globally due to the sparse spatiotemporal distribution of station-derived observations that can be used as a test bed [26]. It is found that GPM IMERG performs better than other satellite products in reproducing spatiotemporal patterns and variability of extreme precipitation [35]. Attributing to gauge adjustment, the systematic differences over land in IMERG data are much smaller compared to those over ocean [26], but the precipitation amount and frequency ratios in the IMERG data are usually overestimated [34]. This study aimed to comprehensively evaluate IMERG data using hourly gauge observations collected from several meteorological institutions. The meteorological stations were distributed over the main land areas including North America, Europe and Asia, and the study period involved the latest two decades (2001-2020). Specifically, the proportion of precipitation hours and hourly precipitation intensity were compared between IMERG data and meteorological observations. The regional differences between the two datasets were compared and analyzed based on several error indicators. The ability of IMERG data to accurately reflect the diurnal cycle and interannual variability of hourly precipitation was also evaluated.