A combined extended triple collocation and cumulative distribution function merging framework for improved daily precipitation estimates over mainland China

Accurate monitoring of daily precipitation is essential for many applications, such as simulation and prediction of watershed water-related disasters. However, precipitation data from satellite and gauge-based precipitation products face challenges in capturing true observations because of their large uncertainties. To improve daily precipitation estimates, this study proposed a new merging framework, ETCCCDF, that combines an extended triple collocation (ETCC) with maximized correlation and cumulative distribution function (CDF) matching with reduced bias. The ETCCCDF framework was applied to systematically integrate three independent precipitation products from SM2RAIN-ASCAT satellite soil moisture estimator, IMERG Early Run satellite retrieval, and CPC unified gauge-based analysis. A new merged precipitation product, with a fine spatiotemporal resolution of 0.25°/1 d, was developed and then thoroughly evaluated using 2304 stations over mainland China from 2007 to 2014. The results showed that the ETCCCDF framework significantly improved the precipitation representation in terms of the correlation, bias, and absolute difference, and it increased modified Kling-Gupta efficiency value relative to any of the parent precipitation products, demonstrating the effectiveness of the proposed merging framework. In addition, the ETCCCDF framework is more robust than the ETCC, triple collocation (TC) merging, and combined strategy of TC with CDF, in which CDF matching successfully corrects the precipitation biases in collocation fusions. The accuracy of the merged products in heavy rainfall estimation during Typhoon Morakot in 2009 and Typhoon Fitow in 2013 was further evaluated. The results confirmed that the ETCCCDF-merged product performed satisfactorily for heavy rainfall events and presented superior statistical scores. This study proves that the ETCCCDF framework is a promising solution for obtaining high-quality precipitation data, particularly for limited-gauge areas.