**5. Conclusions**

Recently, the GSMaP algorithm developers proposed a parameterized gauge calibration method to reduce the errors in GSMaP\_NRT without jeopardizing its near-real-time availability. In this study, we compared and validated the calibrated GSMaP\_Gauge\_NRT product with the original GSMaP\_NRT over the Mainland China, by using a high-quality ground gauge reference dataset.

Our analyses showed that the GSMaP\_NRT product can well-capture spatial patterns of precipitation across the China, but it significantly overestimates the reference precipitation with BIAS of 15.84%. After bias adjustment, this overestimation was obviously reduced, with slight overestimation for GSMaP\_Gauge\_NRT (4.46%). Correspondingly, the value of CC rose from 0.58 for GSMaP\_NRT to 0.67 for GSMaP\_Gauge\_NRT, and the RMSE was reduced from 9.11 mm to 7.07 mm. This indicates that the parameterized calibration strategy can effectively decrease the bias in the GSMaP\_NRT, and that the calibrated GSMaP\_Gauge\_NRT has a better performance than the original GSMaP\_NRT.

In terms of the contingency table statistics, we found that the improvements in the contingency table statistics were not obvious. This suggests that the calibration can effectively reduce the bias but is not good at improving the skill of detecting precipitation events. When we decomposed satellite precipitation into different rainy events, the results further validated that the correction scheme mainly occurred in the hit event and could hardly make up the rainfall missed by the satellites. Thus, we highlight that incorporation of precipitation components is of vital importance for future calibration work.

Finally, our evaluation was extended to the global scale to examine the performance of GSMaP\_Gauge\_NRT from a broader perspective. The global analysis showed that the bias in GSMaP\_ NRT was generally alleviated after gauge calibration and the calibrated GSMaP\_Gauge\_NRT product was in good agreement with the GSMaP\_Gauge product. Therefore, to summarize, all of the results in this study suggest that GSMaP\_Gauge\_NRT can effectively reduce the uncertainties in GSMaP\_NRT after the calibration and that the GSMaP\_Gauge\_NRT is a more reliable near-real-time satellite precipitation product than the original GSMaP\_NRT. As a preliminary assessment of GSMaP\_Gauge\_NRT product, we hope that this study provides useful information for algorithm developers and product users. Considering the diverse nature of the world's topography and climate characteristics, future studies are encouraged to evaluate and validate the performance of GSMaP\_Gauge\_NRT product in more regions using local density gauge networks.

**Author Contributions:** D.L. and B.Y. designed the framework of this study; D.L. performed the experiments and wrote the draft of the manuscript; B.Y. supervised the research and revised the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was financially supported by National Key Research and Development Program of China (2018YFA0605402) and the National Natural Science Foundation of China (51979073, 91647203).

**Acknowledgments:** The authors are grateful to the GSMaP science team in the JAXA for making satellite precipitation data available, and thank to the CMA for providing ground-based precipitation data. Additionally, the authors wish to extend their appreciation to the editors and four anonymous reviews for their thoughtful comments and insightful suggestions.

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