**1. Introduction**

Precipitation is a critical hydrometeorogical variable that plays a key role in energy and water cycles, and thus impacts the weather, climate, hydrology, ecosystem, and Earth system [1–3]. Precipitation is closely bound to life on Earth, due to it being the major source of freshwater [4]. As a result, its measurement is the focus for various disciplines (e.g., atmospheric sciences, ecology, hydrology, agriculture, and economy), in spite of their differences [2,5–7]. For instance, continuous and long-term precipitation observations are necessary for scientific research, but also for informing policy-makers of suitable measures to mitigate the adverse impacts of climate change, especially for droughts and floods [8–12]. Despite its exceptional importance, there still exist great challenges related to obtaining reliable precipitation observations with a long enough time span and large enough space coverage [13–15].

It is well-known that the most direct pathway to obtain precipitation data is through in situ measurements with different gauges (e.g., tipping-bucket rain gauges; [16]). Gauge observations have been extensively utilized by different sectors (e.g., agriculture, industry, and forestry), and have greatly promoted precipitation-related scientific disciplines, e.g., climate studies [10,17,18]. Despite that, we should note that gauges are related to high variability of the rain-bearing systems at different spatio-temporal scales and have an uneven spatial distribution [19,20]. These factors limit the representativeness of gauge precipitation observations to a large extent, and introduce uncertainty into gauge data-based conclusions. With the development of radar technology over past decades, technologically-sophisticated precipitation algorithms based on radar radiance signals have been developed and various corresponding precipitation estimates have been proposed [21–23]. Radar-based precipitation products undoubtedly have the potential to solve or at least reduce the limitations of gauge measurements; for example, they have a more extensive coverage and higher spatio-temporal resolution, which are critical for the analysis of hydrometeorological processes, especially extremes such as flash floods and droughts [2,24]. However, considering the high installation and maintenance costs of radars, the shorter time span of radar observations, and topography-induced radar blockage, radar-based precipitation products are limited and unavailable for some regions, e.g., the nearly negligible radar estimates overseas [14,22,25].

Recently, satellite technology and numerical models/reanalysis systems have developed significantly; therefore, satellite-based precipitation retrievals and precipitation estimates from the models/reanalysis systems have become increasingly attractive and available [3,5,26–31]. Satellite-based precipitation data are derived using various statistical and/or physics-based retrieval algorithms with the radiance information from the satellite-carried sensors, including VIS/IR sensors on geostationary (GEO) and low Earth orbit (LEO) satellites, and passive (PMW) and active MW sensors on LEO satellites. Satellite-based products generally include three types, i.e., VIS/IR- and MW-based estimates, and data through combining the VIS/IR and MW information. Examples include the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) from the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC; [32]), the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN; [33,34]), the PERSIANN-Cloud Classification System (CCS; [13,35]), the PERSIANN-Climate Data Record (CDR; [25]), the Global Satellite Mapping of Precipitation (GSMaP) Microwave-Infrared Combined Reanalysis (RNL; [32,36]) of the Earth Observation Research Center (EORC) of the Japan Aerospace Exploration Agency (JAXA), the National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center (CPC) morphing technique (CMORPH; [37]), the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG; [38]), the Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS; [39]), and a new global Multi-Source Weighted-Ensemble Precipitation (MSEWP) rainfall dataset [40]. Notably, satellite-based precipitation products can only be dated back to the beginning of the satellite era. As an important alternative, reanalysis precipitation data, which are simulated using new atmospheric models combined with advanced data assimilation systems [41–45], provide a means to fulfill the specific requirements (i.e., centennial and even longer precipitation records) of the studies. Additionally, reanalysis systems provide the nearly realistic atmosphere circulation fields, which makes it possible to do many things, including understanding precipitation changes from the perspective of atmospheric dynamical mechanisms [46–48]. The most frequently used reanalysis precipitation datasets include the National Centers for Environmental Prediction reanalysis (NCEP1 and NCEP2; [42,43]), NCEP Climate Forecast System Reanalysis (CFSR; [49]),

ECMWF Reanalysis-5 (ERA-5; [46]) and ERA-Interim [41], the Japanese 55-year Reanalysis Project (JRA-55; [44]), and the National Aeronautics and Space Administration (NASA) Modern Era Reanalysis for Research and Applications (e.g., MERRA-2; [45]).

Before using these products, it is of paramount importance to determine the reliability of the precipitation products using dependable reference datasets, because the inherent uncertainties within these products would likely affect final results, adversely impacting confidence levels [42,50–56]. In terms of a study's specific needs and goals, the satellite-based and reanalysis precipitation datasets have been widely evaluated at different spatio-temporal scales with a series of validation metrics (e.g., [6,50,56–68]). For instance, Sun et al. [6] selected several continuous and categorical validation statistics combined with bias and error decomposition techniques to assess the performance of the PERSIANN-Climate Data Record (CDR) precipitation product in the Huai River Basin, China, and pointed out that the daily, monthly and annual performance of this product varied in accordance with obvious intra-annual cycles. Huang et al. [62] systematically assessed five satellite-based precipitation products (CMORPH, PERSIANN and TRMM3B41RT, TRMM3B42RT, and TRMM3B42) with observations at 2400 weather sites across China, and found that estimates generally captured the overall spatial-temporal variation of precipitation, especially for warm seasons and humid regions. Beck et al. [40] compared 22 gridded daily precipitation datasets across the globe during 2000—2016 with daily observations at 76086 gauges and hydrological modeling, and highlighted that there existed large differences in the accuracy of precipitation estimates and more attention should be paid for precipitation dataset selection in both research and operational applications. de Leeuw et al. [65] used the daily precipitation observations from England and Wales to evaluate the ERA-Interim products, and found that this dataset underestimated the observations on a daily scale, while it could capture the statistics of extreme precipitation events. Lorenz and Kunstamann [67] analyzed the hydrological cycle with three state-of-the-art reanalyses (ERA-Interim, MERRA-2, and CFSR), and demonstrated that large differences existed between the reanalyses and the observations.

The previous evaluations have provided valuable information for the theoretical understanding and improvement of satellite-retrieved algorithms and reanalysis systems. Nonetheless, most were conducted using daily, monthly and annual reference precipitation data; thus, the information about the capacity of the satellite-based and reanalysis precipitation is scarce on a sub-daily scale, especially for China. In fact, there are evident differences in the mechanisms of precipitation within one day, which are closely related to thermodynamic and dynamic processes of water and energy fluxes [3,69–74]. For example, results from Yu et al. [74] indicated that long-duration stratiform precipitation frequently occurred in the early morning during the warm season over central-eastern China, while the late afternoon experienced a higher frequency of short-duration convective precipitation. Therefore, evaluating the multi-source precipitation products with sub-daily observations (daytime and nighttime datasets at least) could provide more detailed information, e.g., flexibility for a precipitation product on sub-daily scale. This is very useful to further improve satellite-based algorithms and models/reanalysis systems from the perspective of sub-daily precipitation mechanisms, and even correct the precipitation products using the sub-daily rather than daily measurements. Additionally, sub-daily precipitation changes have become a hot topic in current research, and numerous studies have been conducted (e.g., [71,75–82]). Cheng et al. [71] pointed out that on annual and seasonal scales (except during spring), the majority of meteorological station records (1961–2006) of Southwest China displayed downward trends for total, daytime, and nighttime precipitation. Lin et al. [80] analyzed characteristics of summer precipitation diurnal variations during 2001–2014 in the Hubei Province of China, and suggested that the diurnal variations existed obvious regional differences. Based on observational day and night precipitation during 1961–2005 across Xinjiang, China, Han et al. [81] concluded that the annual increasing trends of precipitation in the daytime and nighttime respectively accounted for 49% and 51% of the total increasing trend in annual precipitation. Liu et al. [82] found that with the CMORPH dataset during 2008–2014, both the daytime and nighttime precipitation were detected to increase in summer over the Qilian Mountains, China. Lenderink et al. [77] reported that hourly precipitation

extremes have substantially increased in the last century over De Bilt, Netherlands, and Hong Kong, China. Thus, an issue arises—can the existing precipitation products capture the linear trends on a sub-daily scale based on different validation metrics? This question has been paid little attention (e.g., [83]), despite the basis to examine precipitation trends with these datasets. Thus, assessments regarding precipitation trends can provide fundamental information to select the reliable products for exploring precipitation changes, particularly for regions with limited or even no observations (e.g., West China in Figure 1).

Considering the gaps in the previous works of precipitation evaluations, we used China as an example to examine the multi-source precipitation products' capacity to detect precipitation linear trends during daytime and nighttime. Thus, the main objectives of this work were to (1) investigate the spatial distribution of precipitation changes using daily, daytime, and nighttime records from 2393 weather sites across China; (2) to quantify the performance of selected products (i.e., six satellite-based and six reanalysis datasets) in detecting precipitation trends on a sub-daily scale with different validation metrics (correlation coefficient, bias, root mean square error, and sign accuracy) through a comparison with gauge observations; and (3) to identify the metric-based optimal products at a sub-daily scale.
