## 4 Conclusions  
This study investigated the impact of retrieval uncertainty and the sampling interval of input microwave soil moisture (i.e., SMAP) on SM2 RAIN precipitation estimates by referring to observed soil moisture in the central TP. Comparisons were made across different soil moisture input sources, including SMAP retrievals from the ascending and descending orbits as well as their combination, and in situ observations sampled from the same overpass time. Findings suggested that:  
1. SM2 RAIN exhibits large uncertainties when SMAP ascending retrievals are used as input (\(RMSE\) = 10.77 mm/5 d), while better performance is observed by SMAP descending retrievals (\(RMSE\) = 8.35 mm/5 d). This may attribute to less sampling interval and reduced retrieval uncertainty in the morning. Additionally, considering both orbits do add value to SM2 RAIN estimates as evidenced by the comparable or improved metrics with the descending orbit. 2. Biased SMAP soil moisture retrieval counts for 0.20 mm/5 d of precipitation estimate uncertainty in average for all different orbits, while the discontinuity of SMAP soil moisture retrieval exhibits remarkably larger impact of about 0.96 mm/5 d in average. In total, they count for 1.16 mm/5 d of precipitation estimate uncertainty in average. 3. For SM2 RAIN estimation with SMAP, it is suggested to use descending or both orbits to achieve better estimation of accumulated precipitation. Meanwhile, efforts are needed to reduce the retrieval uncertainty of SMAP soil moisture to further improve performance of the SM2 RAIN algorithm. Some limitations are recognized in this study. First, this study was carried out only based on a recent release of the SMAP soil moisture, and future analyses using other state-of-the-art soil moisture products with possible improved quality are desired. Second,  
Figure 14: Evaluation metrics of SM2 RAIN-derived precipitation using soil moisture retrievals from SMAP ascending (SMAP_A), descending (SMAP_D), and combined orbits (SMAP_AD) during JJAS 2015–2018. All the metrics are extracted from Figure 7.  
this study assumed surface runoff to be zero, which may be valid at large scales such as the 100 km CTP-SMTMN area. However, it may be necessary to take into account the potential effects of surface runoff at smaller scales. Besides, the PML-V2 (China) evapotranspiration data are used as input for SM2 RAIN, mainly due to its superiority over the global version of the PML-V2 product. While systematic comparison against other available evapotranspiration data is beyond the focus of this study, future investigations are desired to comprehensively study the error sources of the SM2 RAIN algorithm. Despite the above, SMAP satellite soil moisture retrievals hold great potential in estimating precipitation through SM2 RAIN, though they still exhibit uncertainties, mainly underestimation, in regions such as the TP. Currently, the descending orbit or the joint use of both orbits of SMAP retrievals is recommended for estimating precipitation with SM2 RAIN. Meanwhile, future advancements in satellite retrieved soil moisture, including improving the retrieval accuracy and shortening retrieving intervals, are expected to deliver more reliable precipitation estimation through the SM2 RAIN algorithm. As such, SM2 RAIN estimation could serve as complementary to conventional precipitation estimation methods, including atmospheric modeling, satellite remote sensing, and rain gauge observations, and better support wide applications in regional climate studies such as drought monitoring, weather forecasting, and crop management, etc. This paper is a collaborative work by all of the authors. Conceptualization, L.Z. ; Data curation, K.Z. ; Formal analysis, K.Z. ; Methodology, K.Z. and L.Z. ; Supervision, L.Z., K.Y., L.S., X.N., X.H. and L.F.; Writing (original draft), K.Z. ; Writing (review and editing), K.Z., L.Z. and M.M. All authors have read and agreed to the published version of the manuscript.