The rainfall product derived from the SM2RAIN (Soil Moisture to Rain) algorithm has been widely used. However, there is still a large uncertainty partly due to the soil moisture input and parameters estimation of the SM2RAIN algorithm, which limits the application of the model in alpine regions. Here, the SM2RAIN-BayesOpt algorithm was developed by integrating the SM2RAIN algorithm and Bayesian optimization to improve the estimation of parameters (Z, a, b, T_base, T_pot), subsequently incorporating SMAP Level-3 soil moisture products for rainfall estimation. The performance of the SM2RAIN-BayesOpt algorithm was evaluated based on observed rainfall data under different environmental conditions in three typical alpine regions, namely Tibetan Plateau, Heihe River Basin, and Shandian River Basin. Moreover, SM2RAIN-BayesOpt, IMERG-V06B, and ERA5 reanalysis rainfall estimates were also compared with in-situ rainfall observations. The results showed that the proposed SM2RAIN-BayesOpt algorithm can obtain more accurate rainfall estimates in all studied areas in terms of different evaluation metrics. It was also found that our proposed SM2RAIN-BayesOpt algorithm performs better in alpine meadows and grassland than in desert and forestland. SM2RAIN-BayesOpt algorithm can considerably improve the accuracy of rainfall estimation, and it is of significant value for rainfall monitoring in alpine regions where observational data are scarce.

从 SM2RAIN（土壤水分到雨水）算法导出的降雨量产品已被广泛使用。然而，SM2RAIN算法的土壤水分输入和参数估计仍存在较大的不确定性，限制了该模型在高寒地区的应用。这里，SM2RAIN-BayesOpt算法是通过集成SM2RAIN算法和贝叶斯优化改进参数估计（Z,a,b, _Tbase , _Tpot)，随后结合 SMAP Level-3 土壤水分产品进行降雨量估算。基于青藏高原、黑河流域和山甸河流域三个典型高寒地区不同环境条件下的观测降雨数据，评估了SM2RAIN-BayesOpt算法的性能。此外，还将 SM2RAIN-BayesOpt、IMERG-V06B 和 ERA5 再分析降雨估计值与现场降雨观测结果进行了比较。结果表明，所提出的SM2RAIN-BayesOpt算法可以根据不同的评估指标在所有研究区域获得更准确的降雨估计。还发现我们提出的 SM2RAIN-BayesOpt 算法在高寒草甸和草地中的表现优于在沙漠和林地中的表现。