In this study, temporal trends and spatial patterns of extreme temperature change are investigated at 352 meteorological stations in China over the period 1956–2013. The temperature series are first examined for evidence of long-range dependence at daily and monthly time scales. At most stations there is evidence of significant long-range dependence. Noncrossing quantile regression has been used for trend analysis of temperature series. For low quantiles of daily mean temperature and monthly minimum value of daily minimum temperature (TNn) in January, there is an increasing trend at most stations. A decrease is also observed in a zone ranging from northeastern China to central China for higher quantiles of daily mean temperature and monthly maximum value of daily maximum temperature (TXx) in July. Changes of the large-scale atmospheric circulation partly explain the trends of temperature extremes. To reveal the spatial pattern of temperature changes, a density-based spatial clustering algorithm is used to cluster the quantile trends of daily temperature series for 19 quantile levels (0.05, 0.1, …, 0.95). Spatial cluster analysis identifies a few large clusters showing different warming patterns in different parts of China. Finally, quantile regression reveals the connections between temperature extremes and two large-scale climate patterns: El Niño–Southern Oscillation (ENSO) and the Arctic Oscillation (AO). The influence of ENSO on cold extremes is significant at most stations, but its influence on warm extremes is only weakly significant. The AO not only affects the cold extremes in northern and eastern China, but also affects warm extremes in northeastern and southern China.

Extratropical cyclones (ETCs) over the Northwest Pacific are identified and tracked by applying an objective tracking algorithm to the 6-h time interval relative vorticity fields from the European Center for Medium-range Weather Forecasts (ECMWF) reanalysis data. A total of 2145 ETCs in the November–February winters for the period of 1979–2018 were identified. First, by comparing the ETC track densities in the two periods 1979–1998 and 1999–2018, a significant difference in ETC track density was detected over the Japan Sea, Japan, and North Pacific. Next, the influence of five teleconnection patterns, the El Niño-Southern Oscillation (ENSO), Western Pacific (WP), North Atlantic Oscillation (NAO), Pacific/North American (PNA), and Pacific Decadal Oscillation (PDO) on the ETC track density was investigated by using composite analysis and correlation analysis. WP, NAO, and PDO patterns contributed more to the interannual variability of ETC track density, and higher cyclone densities usually occur in the negative phase of these teleconnection patterns. In contrast, the effects of ENSO and PNA were relatively weak. Finally, a Poisson generalized linear regression model for the ETC counts with respect to the five teleconnection indices was also developed for the purpose of linking all the teleconnection patterns with ETC activities over Northwest Pacific; and the Bayesian model averaging (BMA) procedure was used for model selection in the statistical modeling. The influence of the five teleconnection patterns on ETC track density was also verified in the regression model. The predicted winter ETC counts agreed well with the historical records during 1979–2018 over the Northwest Pacific.

Green tide beaching events have occurred frequently in the Yellow Sea since 2007, causing a series of ecological and economic problems. Satellite imagery has been widely applied to monitor green tide outbreaks in open water. Traditional satellite sensors, however, are limited by coarse resolution or a low revisit rate, making it difficult to provide timely distribution of information about green tides in the nearshore. In this study, both PlanetScope Super Dove images and unmanned aerial vehicle (UAV) images are used to monitor green tide beaching events on the southern side of Shandong Peninsula, China. A deep learning model (VGGUnet) is used to extract the green tide features and quantify the green tide coverage area or biomass density. Compared with the U-net model, the VGGUnet model has a higher accuracy on the Super Dove and UAV images, with F1-scores of 0.93 and 0.92, respectively. The VGGUnet model is then applied to monitor the distribution of green tide on the beach and in the nearshore water; the results suggest that the VGGUnet model can accurately extract green tide features while discarding other confusing features. By using the Super Dove and UAV images, green tide beaching events can be accurately monitored and are consistent with field investigations. From the perspective of near real-time green tide monitoring, high-resolution imagery combined with deep learning is an effective approach. The findings pave the way for monitoring and tracking green tides in coastal zones, as well as assisting in the prevention and control of green tide disasters.