**1. Introduction**

As one of the most active variables in atmospheric circulation, precipitation is a critical linkage between global water and energy cycles. Obtaining spatiotemporal information on precipitation is of grea<sup>t</sup> importance for water resource management, climatological modeling, and many other applications [1–3]. Therefore, reliable precipitation datasets gathered from different sources, including ground stations, ground-based weather radars, and satellites, are essential [4,5].

Collecting precipitation information from ground rain gauge stations is the traditional and common method of measurement. However, the limitations are obvious due to the uneven spatial distribution of the stations. The measurements of ground stations are usually very sparse over some regions of the earth (e.g., the Tibetan plateau), which are meteorologically important [6,7]. As for ground-based weather radars, they have certain superiorities when observing precipitation in local areas. Nevertheless, due to the limitations of the scope of observation and the huge cost of equipment acquisition and maintenance, ground-based weather radars are not the first choice for large-scale precipitation observations.

However, precipitation information obtained from satellites does not meet such limitations. Satellite-based precipitation datasets can depict the spatial and temporal variability of precipitation with a considerable accuracy over regions that have few ground stations [5,8]. Over the last four decades, the progress of meteorological satellites has made it possible for scientists to acquire reliable and cost-effective precipitation datasets through a variety of sensors and inversion algorithms [9–13]. Therefore, obtaining high-resolution and accurate precipitation estimates derived from sensors on satellites at a regional or global scale has become a highly-efficient research method at present [4,14,15].

Satellite-based precipitation products provided by several institutions and organizations from all over the world are different in terms of their spatial and temporal resolution, data coverage, data continuity, and latency [16]. The products mentioned above can only be used for practical applications if there is a consistency in terms of both the spatial and temporal scales with ground-based measurements. Therefore, the validation of satellite-based precipitation products is necessary to ensure the reliability of the products. In addition, in order to provide product users with a reliable error structure and instructions for satellite precipitation products, as well as a reasonable advancement of retrieval algorithms, validation is indispensable for satellite-based data applications [17].

There have been numerous studies evaluating the performance of satellite-based precipitation products. Datasets such as Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS), the Climate Prediction Center (CPC) MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Multi-Source Weighted-Ensemble Precipitation (MSWEP), H-SAF (EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management) have been validated in various regions of the world [18–23]. Chen et al. [24] analyzed the similarities and differences between TMPA V6 and V7 over China, and determined that 3B42 RT V7 overestimated precipitation over the Qinghai–Tibet Plateau by approximately 139.5%. Teng et al. [25] identified overestimates outside the 95% prediction interval in TMPA data for the Xin'anjiang Reservoir, which is the largest artificial water body in southeast China. Prakash et al. [26] evaluated the accuracy of IMERG data with TMPA and Global Satellite Mapping of Precipitation (GSMaP) data in southeast India. The results showed that IMERG represented large-scale monsoon rainfall features and their variability more realistically. Tang et al. [22] evaluated IMERG from April to December 2014 at hourly scale over mainland China and found that IMERG performed with a small correlation coefficient (CC) of ~0.40 and slight overestimates by an average of ~9%. Katiraie-Boroujerdy et al. [27] found that PERSIANN-CDR agreed well with gauge-based datasets at monthly scales over Iran, with a CC of ~0.88. Rivera et al. [18] demonstrated the systematic errors that could be attributed to the varying performance of CHIRPS in different seasons over Argentina, such as the significant bias of ~65.8% over the north Patagonia region.

Although there are a large number of evaluation studies on satellite-based precipitation products, few investigations have been conducted to assess the quality of the precipitation products from Chinese Fengyun (FY) series satellites. FY series satellites are the major operational meteorological satellites of China. Currently, there are eight on-orbit FY satellites in operation, including three polar orbit satellites and five geostationary satellites, in order to provide global meteorological observation services. With the increasing influences of FY series satellites, evaluating the performance and usability of their precipitation products has become increasingly necessary.

Compared with the data obtained from polar orbit satellites, precipitation information from geostationary satellites has a fixed observation area and stable observation intervals, which can better reflect the spatial distribution of precipitation and its changes at hourly and other temporal scales in the study areas. In other words, geostationary satellites have not only the spatial continuity of most other satellites, but also the temporal continuity of ground stations. Therefore, we selected two of the main current satellite-based precipitation products from two geostationary satellites in different batches of the FY-2 series to evaluate their quality in this study. The main objects of this study are as follows: (1) To firstly evaluate and compare the precipitation products from FY-2 and GPM at meteorological scales (hourly, daily) and a climatological scale (monthly), respectively, and (2) to analyze the potential error sources of the main current satellite-based precipitation products over mainland China in summer, 2018.

#### **2. Study Area and Datasets**
