**1. Introduction**

Precipitation is an important component of the hydrological cycle [1,2]. Accurate and high-resolution precipitation data are crucial in different fields such as weather forecast, disaster preparation and prevention, and water resource management [3,4]. The quality and resolution of precipitation inputs can also significantly affect the performance of various hydrological, climatic, and atmospheric models [5].

However, obtaining suitable precipitation data could be challenging for researchers as well as practitioners. Availability of traditional ground observations has been limited because of the inadequate and uneven distribution of rain gauges, especially in developing countries, mountainous and remote areas, and over oceans [6]. On the other hand, although weather radar products can provide rainfall observations over a wide region [7], they are subjected to both random and systematic errors [8–11]. Random errors could arise from the sub-grid horizontal and vertical variability of rainfall and the noise of the radar hardware system, while systematic errors may originate from sources such as drifts in radar calibration constant, systematic variations in the reflectivity–rain-rate relationship, and strong gradients in the reflectivity profile [12]. The presence of complex topography may further amplify some of the error sources [13].

In recent years, with the rapid development of remote sensing techniques, satellite precipitation products (SPPs) have been increasingly applied in monitoring precipitation patterns [14–16]. Deriving precipitation products through satellite remote sensing has the advantages of wide coverage and high spatiotemporal resolution, which complement traditional ground gauge measurements. For example, the Tropical Rainfall Measuring Mission (TRMM) satellite launched in 1997 has been extensively used in hydrological modelling and climate change studies. Li et al. [17] found overall good linear relationships between TRMM and ground rainfall observations at both daily and monthly time steps in the Xinjiang catchment, China. Bharti and Singh [18] compared TRMM 3B42V7 with the gauge-based measurements at different altitudes in the northwest Himalayan region. They found that the satellite performed satisfactorily in the altitude range of 1000–2000 m, but poorly over higher-altitude regions at a daily time step.

In 2014, the National Aeronautics and Space Administration (NASA) of U.S. and JAXA (Japan Aerospace Exploration Agency) jointly developed a new generation of Global Precipitation Measurement (GPM) satellites. In addition to inheriting the advantages of the TRMM satellites in detecting precipitation in the tropics, GPM satellites provide global precipitation estimates for a wider quasi-global coverage (60◦ N–60◦ S) at a much higher spatiotemporal resolution (0.1◦ × 0.1◦ and 30-min interval). Much research has concluded that GPM products have improved in terms of both rainfall observation accuracy and hydrological simulation performance compared to TRMM products. For example, Tan et al. [19] and Sharifi et al. [20] compared the accuracy of rainfall observations between IMERG (integrated multi-satellite retrievals for GPM) and TRMM in Singapore and India, respectively. In both studies, all evaluation indices had indicated a better performance of IMERG than TRMM in providing monthly and daily rainfall data. In China, Tang et al. [21] analyzed the errors of IMERG and TRMM products in six sub-regions of Mainland China and found that IMERG had improved the accuracy of precipitation observations in the mid-high latitude as well as arid regions. In addition, they observed that IMERG could better reproduce the probability density function of rainfall, especially in the range of lower rainfall intensity.

In June 2019, the IMERG product was upgraded from Version 5 (V5) to Version 6 (V6) by reducing biases based on the new Global Precipitation Climatology Centre (GPCC) monthly precipitation records. Meanwhile, TRMM data before 2014 have also been reprocessed with the latest algorithm of the IMERG V6. Until now, few studies have been carried out to evaluate the performance of the latest IMERG and TRMM products.

Furthermore, the majority of previous research has evaluated SPP products at the monthly or daily scale, although both GPM and TRMM products contain hourly rainfall products. High quality hourly rainfall data have been found to be valuable to various hydrological applications around the world [22]. For example, Zhou and Wu [23] found that the precipitation intensity and distribution characteristics of typhoons in China could be better analyzed with hourly precipitation than daily observations at automatic weather stations. Yang et al. [24,25] found that the SWAT (Soil and Water Assessment Tool) model built on hourly rainfall could yield much better performance in simulating daily streamflow and monthly nutrient loads than the SWAT model built on daily rainfall in the Upper Huai River basin of China. Boithias et al. [26] found that the SWAT model built on hourly rainfall could better predict discharge over long periods of time than the MARINE model in the Mediterranean coastal Têt River basin (Southwestern France).

Compared to daily rainfall, hourly rainfall data are much more difficult to obtain because of several reasons. Firstly, much fewer gauges can or will record the amount of rainfall at an hourly or finer interval worldwide. Secondly, hourly rainfall data are generally not free to the public. Purchase of hourly rainfall data might be too expensive for researchers or practitioners in some regional studies. Finally, authorities in some regions may consider hourly rainfall records as sensitive data, thus denying their access to the public citing security reasons. In view of the limited access to hourly rainfall data globally, SPPs may provide a much-needed alternative for deriving such products. So far, few studies have been carried out to evaluate the capability of SPPs in providing hourly rainfall estimates.

To fill in the gaps, this study aims to evaluate the accuracy of the latest GPM and TRMM rainfall products across the monthly, daily, and hourly scales based on the ground rain gauge measurements between January 2009 and December 2017 in the Shuaishui River Basin (SRB) of eastern Central China. The Shuaishui River is the headwater tributary to the Qiantangjiang River, the main river flowing across the Zhejiang Province of China. With water quality inferior to the Class III standard at 50.5% of its total river length, the Qiantangjiang River Basin is faced with severe water security concerns [27,28]. As the critical ecological barrier to the Qiantangjiang River, the hydrological conditions of the SRB has direct impact on the downstream ecological environment.

Essentially a hilly watershed, SRB is characterized with complex terrains and obvious vertical height difference. Precipitation in the basin is abundant, but also highly seasonal. Steep slopes combined with ample rainfall in summer have aggravated the risk of natural disasters such as floods and mudslides [29]. The flood in June 2016 in the SRB, for instance, has affected 58,000 people with a direct economic loss of 168 million RMB. SRB, therefore, presents an ideal referencing region for evaluating the suitability of using SPPs in the sub-tropical hilly regions with large inter-annual and intra-annual rainfall variabilities.

#### **2. Study Area**

Approximately 159 km in length, the Shuaishui River originates from the Hutou mountain ranges and flows across the Xiuning County before pouring into the Xinanjiang River at the Tunxi district of Huangshan City. The SRB (117◦39 –119◦26 E and 29◦24 –31◦1 N) has a total area of 1522 km2 (Figure 1). Dominated by a hilly terrain, more than 70% of the basin is at an altitude of above 500 m. Land use and land cover in the basin is mainly forestland and cultivated land, which respectively accounts for 78.9% and 14.6% of the total coverage.

**Figure 1.** Map of Shuaishui River Basin.

Located in the subtropical monsoon climate zone, rainfall in the SRB is usually abundant. Between 2009 and 2017, mean annual rainfall observed by rain gauges in the basin ranges from 1747 mm in 2013 to 2700 mm in 2015 with an overall average of 2278 mm (Figure A1). Within each year, mean monthly rainfall usually increases steadily from January to May and peaks in June. Precipitation in June alone could account for more than one-fifth of annual total rainfall. After June, monthly rainfall falls sharply and exhibits an overall decreasing trend till the end of the year (Figure A2).
