**1. Introduction**

Reliable precipitation estimates are crucial because of their role in flood monitoring, crop yield, and water resource management [1–3]. However, in many regions of Earth, like the oceans, deserts, and mountains, ground-based observing networks from gauges and radars are sparse or even nonexistent, which restricts our understanding of global water cycle and local hydrological processes [4–6]. The recent development of precipitation-retrieval techniques from satellite-based remote sensing makes it possible of measuring precipitation on the global scale. The remote sensing of precipitation combines the advantage of the frequency sampling of infrared (IR) sensors derived from geostationary (GEO) satellites and the superior accuracy (but poor sampling) of passive microwave (PMW) sensors carried onboard the low earth orbiting (LEO) satellites, in an effort to produce precipitation data with extensive spatial coverage and fine resolutions [7–9].

To date, various satellite precipitation missions have been implemented and their products have been made available to the public. Previous satellite precipitation missions include the NASA's Tropical Rainfall Measuring Mission (TRMM [10]), NOAA's Climate Prediction Center (CPC) morphing technique (CMORPH [11]), JAXA's Global Satellite Mapping of Precipitation (GSMaP [12]), the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN [13]), the Climate Hazard Group InfraRed Precipitation (CHIRP [14]), and the successor of TRMM: Global Precipitation Measurement (GPM [3]). These satellite precipitation missions and

products have benefitted the hydrology and meteorology community in relevant researches and applications. However, more recent studies have found that satellite precipitation products contain considerable errors due to the indirect retrieval methods of remote sensing [15–23]. One of the most effective strategies to diminish these errors is combining ground-based data to adjust the satellite precipitation products. For example, the 3B42 product is one of the most popular gauge-adjusted satellite precipitation products at the TRMM era, which incorporates the monthly Global Precipitation Climatology Centre (GPCC) gauge data to increase the accuracy of its original satellite-only 3B42RT product. Today, numerous studies have demonstrated that the 3B42 shows substantial improvement than the 3B42RT, with lower bias and better detection skills [24–28]. However, the 3B42 data is not available in real-time, and researchers must wait ~2.5 months after observation time, while the delay of pure satellite-derived 3B42RT is only 8 h [29]. Obviously, traditional gauge-adjusted schemes depend on the time availability of gauge data. Nevertheless, collecting and processing gauge data in real-time is not possible on a global scale, especially in underdeveloped countries and areas, which delay the availability of satellite precipitation products. The question of the availability exists widely in the gauge-adjusted satellite precipitation products. For example, if someone want to use PERSIANN-CDR or GSMaP\_Gauge data, they must wait ~3 months or ~3 days after observation, respectively [21,30].

In some cases, the real-time availability precipitation data is more critical for applications like rainstorm monitoring and flash flood warning, and it does not seem practical to use the traditionally delayed gauge-adjusted satellite precipitation products [31,32]. Thus, it is important to reduce the error of satellite precipitation estimates as much as possible without jeopardizing its near-real-time availability. To this end, a climatological calibration algorithm (CCA) was proposed in the TRMM Multisatellite Precipitation Analysis (TMPA) real-time system. This method utilizes climatological gauge information to alleviate errors and keep the timeliness of 3B42RT itself [9]. Yong et al. [31] initially investigated the performance of CCA in the 3B42RT precipitation estimates over two different basins of China using a local dense rain gauge. The author found that the systematic errors in 3B42RT were minimized overall after the CCA calibration. Nevertheless, the author also highlighted that the performance of calibrated precipitation became worse in high-latitude areas, or areas beyond the 40◦ latitude belts. In addition, from a global map view of error analysis, Yong et al. [9] demonstrated that the CCA calibrated 3B42RT precipitation has large bias in mountainous regions (especially over the Tibetan Plateau). Theoretically, the CCA is used in the GPM near-real-time runs of the Integrated Multisatellite Retrievals for GPM (IMERG) algorithm. However, considering the unstable performance of CCA, the developers of the IMERG algorithm are re-evaluating the CCA calibration. Meantime, the IMERG near-real-time products do not currently have climatological calibration.

As the Japanese counterpart of IMERG, the GSMaP is another mainstream satellite precipitation product at GPM era, which was produced by reliable physical models and by distributing hourly global precipitation map with 0.1◦ × 0.1◦ resolution [20,33,34]. To satisfy different application requirements, there are two main groups of GSMaP products: Near-real-time and standard products. As the name implies, the near-real-time product is intended to provide available satellite precipitation quickly, while the standard product applies more PMW/IR sources to create relative accurate precipitation estimates. Correspondingly, the near-real-time product has about a 3-h delay, and the standard product has a large latency of about 3 d. To reduce bias on the satellite-derived GSMaP products, gauge-adjusted GSMaP products are developed using ground-gauge measurement as a calibrator. The gauge-calibrated product of standard GSMaP\_MVK is GSMaP\_Gauge, which adjusted by daily CPC gauge data. Many studies have assessed and compared the performances of GSMaP\_Gauge over the last few years and have shown that GSMaP\_Gauge is a satisfactory gauge-adjusted satellite precipitation estimation around the world, especially over East Asia [34–38].

Recently, in the GSMaP project, the GSMaP\_Gauge\_NRT was produced by a GSMaP algorithm team, aiming to improve the accuracy of near-real-time product of GSMaP (i.e., GSMaP\_NRT) and maintain its timeliness. Section 2.2 describes the calibration procedure of GSMaP\_Gauge\_NRT in detail. Thus, it is crucial to understand the performance of new GSMaP\_Gauge\_NRT product timely. In the

official document of GSMaP, the GSMaP developers eagerly encouraged people to evaluate and validate the GSMaP\_Gauge\_NRT in different regions. Therefore, in this study, we systematically assessed the performance of the GSMaP\_Gauge\_NRT precipitation estimates and the original uncalibrated GSMaP\_NRT over the Mainland China. The rest of this paper is organized as follows. In Section 2, we describe the study area, the precipitation data and the error metrics. Then, a presentation of results and discussion in this study are provided in Sections 3 and 4, respectively. Finally, the summary and conclusions are given in Section 5.
