*4.1. Possible Causes for Variation in Performance among Precipitation Products*

In this study, we explored the reliability of the satellite-based and reanalysis products in capturing precipitation linear trends across MC, and found that the performances of these products exhibited clear differences. In general, TRMM3B42 and MERRA-2 showed the best overall performance. There are several possible explanations for the performance variation, e.g., input data, onboard sensors, and retrieval algorithm for the satellite-based products; and numerical models and their structures, parameterizations (especially for schemes about precipitation processes), and assimilation systems for the reanalysis products. Nonetheless, quantitatively identifying the impacts of these factors is difficult and beyond the scope of this study. As a result, we would like to discuss the potential causes of different performance among the precipitation products with the same retrieval algorithm or model structures, i.e., TRMM3B42RT vs. TRMM3B42, PERSIANN vs. PERSIANN-CCS, GSMaP-RNL vs. GSMaP-RNLG, ERA-Interim vs. ERA-5, and NCEP1 vs. NCEP2. It is evident that TRMM3B42 generally outperforms TRMM3B42RT, could be attributed to the fact that the former incorporates rain gauge data (i.e., monthly

GPCP and CAMS data; [32]) to adjust the precipitation estimates. In some WRRs, TRMM3B42RT performed better or was the OP, implying that the TRMM3B42 precipitation trends were occasionally overcorrected due to an inappropriate correction method (e.g., daily TRMM3B42RT adjusted with monthly observation; [32]). For PERSIANN and PERSIANN-CCS, their major differences are that the latter includes a cloud classification system based on cloud height, areal extent, and variability of texture estimated from satellite imagery to more accurately describe the relationship between precipitation rate and brightness temperature [35]. Despite that, the performance of PERSIANN in detecting precipitation trends was better than PERSIANN-CCS across MC based on most of the validation metrics. This indicates that the cloud classification system within PERSIANN-CCS has limited effectiveness in improving the estimated precipitation trends, although PERSIANN-CCS has been found to outperform PERSIANN in estimating precipitation amount over some regions of MC and its sub-regions (e.g., Tibetan Plateau and Yangtze River Basin, [88–91]). For the bias metric, PERSIANN-CCS performed better, mainly because, within a given region, the functions between precipitation rate and brightness temperature are established for each categorization of cloud-patch, and thus the regional biases are more likely to be offset. Relative to other satellite-based products, the two GSMaP products had the worst performance in MC and ten WRRs, indicating that the algorithm employed by GSMaP-RNL and GSMaP-RNLG may be problematic in capturing precipitation trends. Meanwhile, some studies also found that the GSMaP products had very low performance in capturing precipitation magnitudes and hydrological modeling over MC [92] and some Asian regions, such as the VuGia–ThuBon River Basin of Vietnam [93], and Mekong River Basin [94]. Moreover, the worst performance of GSMaP-RNLG in terms of specific validation metric suggests that its gauge-based correction processes are not efficient to adjust the precipitation trend. Relative to ERA-Interim, ERA-5 had a more advanced assimilation system and more and newer observational inputs, and thus was observed to have better performances (e.g., lower bias and root-mean squared error, and higher correlation coefficient) to reproduce precipitation in some regions, [45,91,95]. However, we found that in the study the ERA-5 precipitation trends poorly match the observations relative to the ERA-Interim. These findings are consistent with the findings of Nogueira across the globe [96], who pointed out that the trend of global-mean rainfall in ERA-Interim was closer to GPCP than ERA-5, and suggested that the possible causes were associated with the global energy budget [97,98]. Due to NCEP2 with new system components including simple precipitation assimilation over land surfaces for improved soil wetness [43], NCEP2 precipitation agreed more closely with gauge measurements than NCEP1 data in China [99], USA [100], and Central Equatorial Africa [101]; by contrast, comparisons between NCEP1 and NCEP2 in representing precipitation linear trends show that no obvious differences existed. This may be related to significant time-varying jumps in the late 2000s within NCEP2, mainly due to the changes in observing systems, such as the introduction of new data into the assimilation systems [102,103]; this is also the possible cause of poor performance for JRA-55 [101]. The validation metrics clearly show that, based on precipitation linear trends, MERRA-2 performed better than other reanalysis products and even satellite-based precipitation products in MC. The better performances of MEERA-2 for representing precipitation amount were also found in other regions (e.g., Nepal, and the Pamir region of Tajikistan, [104,105]). Some scholars pointed that the possible causes are related to the advanced data assimilation technique within MERRA-2 and the bias corrections of MERRA-2 precipitation [45]. We should note that for a given product there are differences in performance of detecting precipitation trends within a day (e.g., daytime and nighttime annual correlation coefficients for ERA-Interim) and among seasons (e.g., smaller winter correlation coefficient values for PERSIANN but larger values in the other three seasons), mainly due to the different physical mechanisms controlling precipitation processes [69–71,73,74]. For example, some studies stated that sea–land breeze is closely associated with the diurnal cycle of precipitation in coastal areas, while topography and mountain–valley breeze plays an important role in the interior [73,74]. Therefore, to increase the accuracy of sub-daily and seasonal precipitation estimates, specific algorithms for the satellite-based products and specific model structures for the reanalysis products should be developed.
