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An Extended Triple Collocation for maximized Correlation (ETCC) method was proposed with a unique correlation function, the purpose of which is to maximize the correlation between the merged product and unknown truth. The method was tested over quasi‐global land by combining three independent precipitation products. The performance of the ETCC‐merged product was then evaluated against three reference data sets and compared with the existing Triple Collocation (TC) merging. The merged product was found to be generally superior to each contributor. Moreover, the ETCC method is better able to improve the correlation of merged product compared with the TC approach. Other improvements are also shown in the absolute difference of the ETCC‐merged product, such as regional validation for central North America and mainland China. These demonstrate the effectiveness of the ETCC method, and accordingly, it can provide a promising solution for maximizing the correlation of merged product without the truth. Plain Language Summary: Precipitation plays a significant role in global atmospheric and hydrological modeling systems. Owing to uncertainties in precipitation estimates from a single satellite or reanalysis product, it is necessary and challenging to blend multi‐source precipitation information to improve the quality of precipitation estimates. The present study proposed a new merging method, called Extended Triple Collocation for maximized Correlation (ETCC), which aims at maximizing the correlation of the merged product against unknown truth. The ETCC‐merged product combined three independent precipitation information derived from the satellite and reanalysis products, and then, was evaluated using three reference data sets. Meanwhile, we compared ETCC's performance with that of the original Triple Collocation (TC) merging. Results show that ETCC method not only performs better than each contributor, but also is more effective in improving precipitation accuracy relative to the TC approach especially in central North America and mainland China. Key Points: A new method is proposed for maximizing the correlation between the merged product and unknown truthThe merged product is more accurate than each contributorThe new method performs better than the original triple collocation [ABSTRACT FROM AUTHOR]

Climate change and human activities are two major driving forces affecting the hydrologic cycle, which further influence the stationarity of the hydrologic regime. Hydrological drought is a substantial negative deviation from the normal hydrologic conditions affected by these two phenomena. In this study, we propose a framework for quantifying the effects of climate change and human activities on hydrological drought. First, trend analysis and change‐point test are performed to determine variations of hydrological variables. After that, the fixed runoff threshold level method (TLM) and the standardized runoff index (SRI) are used to verify whether the traditional assessment methods for hydrological drought are applicable in a changing environment. Finally, two improved drought assessment methods, the variable TLM and the SRI based on parameter transplantation are employed to quantify the impacts of climate change and human activities on hydrological drought based on the reconstructed natural runoff series obtained using the variable infiltration capacity hydrologic model. The results of a case study on the typical semiarid Laohahe basin in North China show that the stationarity of the hydrological processes in the basin is destroyed by human activities (an obvious change‐point for runoff series is identified in 1979). The traditional hydrological drought assessment methods can no longer be applied to the period of 1980–2015. In contrast, the proposed separation framework is able to quantify the contributions of climate change and human activities to hydrological drought during the above period. Their ranges of contributions to hydrological drought calculated by the variable TLM method are 20.6–41.2% and 58.8–79.4%, and the results determined by the SRI based on parameter transplantation method are 15.3–45.3% and 54.7–84.7%, respectively. It is concluded that human activities have a dominant effect on hydrological drought in the study region. The novelty of the study is twofold. First, the proposed method is demonstrated to be efficient in quantifying the effects of climate change and human activities on hydrological drought. Second, the findings of this study can be used for hydrological drought assessment and water resource management in water‐stressed regions under nonstationary conditions. [ABSTRACT FROM AUTHOR]