Although satellite-based precipitation estimation has extensive application potential, validation of its reliability is challenging for areas lacking ground-based data which is particularly true for many arid and semiarid regions. The triple collocation (TC) method can be used to evaluate three independent inputs with unknown true values, and thus provides an appealing alternative for assessment of satellite precipitation products in lack of observation regions. This study is the first to utilize TC to comprehensively assess the uncertainties of various satellite precipitation products in Central Asia (CA) with a distinctive continental arid and semi-arid climate. TC requires the errors of inputs to be independent with each other, while many multi-satellite precipitation products use overlapped data sources. To address this problem, this study uses a soil moisture-based product (SM2RAIN) and a reanalysis model-based product (ERA5) as two inputs of a triplet with the last input coming from each one of the six satellite precipitation products (3B42, CHIRPS, CMORPH, GSMaP, IMERG, PERSIANN). Six independent triplets are obtained in this way. The temporal/spatial resolution is daily/0.1° and the period is 2007–2019. The results show the overall performance of GSMaP is best among eight gridded precipitation products over CA, followed by IMERG, CMORPH and PERSIANN. All precipitation products show degraded performance with increasing altitude. Moreover, the accuracy estimates are subjected to uncertainties caused by the TC method and data inputs. Overall, the study concludes the TC method can provide a new perspective for the assessment of precipitation products over data-absent arid and semiarid regions, while careful check and explanation of evaluation results are always necessary to defend the rationality of TC in specific cases.

尽管基于卫星的降水估计具有广泛的应用潜力，但是对于缺乏地面数据的地区，其可靠性的验证仍具有挑战性，这在许多干旱和半干旱地区尤其如此。三重配置（TC）方法可用于评估三个具有真实值未知的独立输入，从而为评估缺乏观测区域的卫星降水产物提供了一种有吸引力的替代方法。这项研究是首次利用TC来全面评估具有独特大陆干旱和半干旱气候的中亚（CA）各种卫星降水产品的不确定性。TC要求输入的误差彼此独立，而许多多卫星降水产品使用重叠的数据源。为了解决这个问题，这项研究使用土壤水分基产品（SM2RAIN）和基于再分析模型的产品（ERA5）作为三元组的两个输入，最后一个输入来自六个卫星降水产品（3B42，CHIRPS，CMORPH，GSMaP中的每个） ，IMERG，PERSIANN）。以此方式获得六个独立的三胞胎。时空分辨率为每日/0.1°，周期为2007–2019。结果表明，GSMaP的总体性能在超过CA的八个网格降水产品中最好，其次是IMERG，CMORPH和PERSIANN。随着海拔的升高，所有降水产品的性能都会下降。此外，精度估计会受到TC方法和数据输入所带来的不确定性的影响。总体，