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In recent years, climatology, variability, hydrological impact, and climatic drivers of atmospheric rivers (ARs) are widely explored based on various AR identification algorithms. Different algorithms, varying in their tracing variables, thresholds, and geometric metrics criteria, will introduce uncertainty in further study of AR. Herein, a novel AR identification algorithm is proposed to address some current limitations. A coupled quantile and Gaussian kernel smoothing technique is proposed to make a balance in capturing the spatiotemporal variation of integrated water vapor transport climatology and avoiding largely biased estimation. In spite of variety of AR shape, orientation, and curvature, more reliable AR metrics (e.g., length and width) can be calculated based on the generated smooth AR trajectory, which is realized by modifying and integrating the concepts of local regression and K-nearest neighbors. An unprecedented and novel metric (i.e., turning angle series) is delivered to quantify AR curvature, serves as the key to distinguish tropical cyclone-like features, which often indicate occurrences of tropical cyclones. It also bridges ARs to their associated atmospheric circulation patterns. A pilot application of the algorithm is presented to identify persistent AR events related to flood triggering extreme precipitation sequences in the Yangtze River Basin (YRB). A dominating AR route, which connects Arabian Sea, Bay of Bengal, South China Sea, to Southeast China and YRB, terminates in the North Pacific, is found principal to the flood triggering extreme precipitation sequences in the YRB. In addition, this algorithm is extensible to other regions, even global domain.

Plain Language Summary Atmospheric rivers (ARs), which are characterized as long and narrow corridors in the lower atmosphere that transport moisture, have received a lot of attention in many regions, mainly because of their close relationship with hydrometeorological extremes (e.g., heavy rainfall and flooding). Several AR identification algorithms based on different proxies were developed for different regions; however, uncertainty and limitations still exist. We present a novel AR identification algorithm to address some of these limitations to aid in the understanding and prediction of AR. In spite of varying AR shape, orientation, and curvature, this algorithm provides more robust AR detection threshold, smooth AR trajectory, and reliable AR geometric features. In particular, it offers a novel AR metric (i.e., turning angle series) to differentiate tropical cyclone-like features from ARs and build a linkage between AR and atmospheric circulation patterns, which is unprecedented in AR studies. As a demonstration of the algorithm, we identify the associated AR events during flood triggering extreme precipitation sequences in the Yangtze River Basin (YRB). A dominating AR route, which connects Arabian Sea, Bay of Bengal, South China Sea, to Southeast China and YRB, terminates in the North Pacific, is found principal to the flood triggering extreme precipitation sequences in YRB.