**6. Conclusions**

Evaluations of satellite-based quantitative precipitation estimates are of grea<sup>t</sup> importance when applying these datasets in related fields, such as hydrology, meteorology, and agriculture. In this study, we firstly evaluated and compared the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Mission (GPM), respectively, over mainland China in summer, 2018. The main conclusions are as follows:

(1) The three products (FY-2E QPE, FY-2G QPE, and IMERG) demonstrate similar spatial precipitation patterns; for example, a general decreasing trend from the southeast to northwest over mainland China;

(2) Compared with rain gauge measurements, FY-2G QPE and IMERG perform better among the three products, with the CC varying from 0.65 to 0.90 and 0.80 to 0.90 in summer, 2018, followed by FY-2E QPE (CC of ~0.40 to 0.53);

(3) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which might be caused by the algorithm characteristics of IMERG Final-run products;

(4) Compared with ground observations, FY-2G QPE exhibits underestimates in capturing the precipitation at both a monthly and hourly scale, while FY-2E QPE and IMERG generally tend to overestimate the precipitation in summer, 2018;

(5) The performances of both FY-based and GPM-based precipitation products are poorer during the period from 06:00 to 10:00 UTC than other periods at diurnal scale, which might have resulted from the satellite-based precipitation retrieval algorithms and the impact of regional meteorological and climatological influences. Further study is required to investigate the underlying reasons for this phenomenon;

(6) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of ~0.81), which means that these two products have similar capacities to capture the spatial patterns of precipitation events.

The findings presented in this study could provide valuable preliminary references for improving the current satellite-based QPE retrieval algorithms for the next generation.

**Author Contributions:** Conceptualization, J.X., Z.M., and Z.S.; methodology, J.X., Z.M., and G.T.; software, X.M., W.W., and Q.J.; validation, J.X., G.T., and Q.J.; formal analysis, J.X., Q.J., and X.M.; investigation, J.X., Z.M., G.T., and Q.J.; resources, J.X., X.M., and W.W.; data curation, J.X. and W.W.; writing—original draft preparation, J.X. and Z.M.; writing—review and editing, J.X. and Z.M.; visualization, J.X., Q.J., X.M., and W.W.; supervision, Z.M., G.T., and Z.S.; project administration, Z.M. and Z.S.; funding acquisition, Z.M. and Z.S.

**Funding:** This research is financially supported by the National Key Research and Development Program (2017YFD0700501); the State Key Laboratory of Resources and Environmental Information System, National Natural Science Foundation of China (41571339, 41901343); the Open Fund of the State Key Laboratory of Remote Sensing Science (OFSLRSS201909), the Key R&D Program of Ministry of Science and Technology (2018YFC1506500); and the China Postdoctoral Science Foundation (2018M630037 and 2019T120021).

**Acknowledgments:** The authors' grea<sup>t</sup> gratitude is extended to the NSMC for providing FY-2 QPE and to the CMA for providing ground observations. We also appreciate the NASA/Goddard Space Flight Center's Mesoscale Atmospheric Processes Laboratory for providing the IMERG data.

**Conflicts of Interest:** The authors declare no conflicts of interest.
