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Tropical cyclones (TCs) constitute one of the major types of meteorological disasters in China, causing extensive damage. Based on the latest quality‐controlled provincial‐level TC disaster losses data over China from 2001 to 2020, the spatiotemporal variation in TC disaster and its link with TC activity in the form of prevailing track, precipitation, and intensity were investigated. Results showed that the areas impacted by TCs expanded significantly in the past decade (2011–2020) compared with the previous decade (2001–2010) throughout China, while the direct economic losses resulting from TCs did not increase significantly. Regionally, TC disaster losses increased in Northeast China but decreased in Fujian province. Causal analysis indicated that the increased influential TC (ITC) frequency and TC precipitation together led to the increase in damage for northern China. Also, more westward ITCs resulted in the increase in TC disasters for southwestern China. For southeastern China, the reduction in TC disasters was related to the northward shift in the northwestward ITC track. Although TC disasters increase with exposure, they are heavily influenced by TC track, precipitation, and intensity. Our findings suggest that more attention should be paid to TC prediction and early warning in northern China, especially Northeast China, where TC disasters are growing but there is greater vulnerability and insufficient capacity for TC prevention and mitigation. Plain Language Summary: China is one of the most frequently affected countries by tropical cyclones (TCs) in the world, suffering huge losses in term of human life and property. The average number of influential TCs varies little from year to year, but TC disasters vary broadly. These TC disasters involve not only the features of TCs themselves, such as precipitation, winds, and storm surges, but also socioeconomic development, the capacity of disaster prevention and preparedness. Utilizing the latest data on TC disasters, this study's results reveal that TC disasters have been aggravated in low‐impact areas, such as Northeast China. Understanding these changes in the characteristics of TC disasters is essential for TC disaster mitigation and climate change adaptation. Key Points: Tropical cyclone (TC) impacted areas expanded significantly in the past decade (2011–2020) compared with the previous decade (2001–2010) throughout ChinaTC disasters increased in Northeast China, where there is greater vulnerability and insufficient capacity for TC prevention and mitigationTC disasters are heavily influenced by TC track, precipitation, and intensity [ABSTRACT FROM AUTHOR]

ABSTRACT In the present study, the performance of the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Atmospheric Model Intercomparison Project (AMIP-II) reanalysis ( NCEP-2) and NCEP Climate Forecast System Reanalysis ( CFSR) seasonal temperature data for China was quantitatively evaluated by using detrended fluctuation analysis. The results indicate that the quality of the NCEP-2 temperature data is the highest in autumn, while the quality of CFSR data set is the highest in summer. Both the quality of NCEP-2 and that of CFSR temperature data is the lowest in winter. The quality is very low in eastern Tibetan Plateau and most of Xinjiang at a significance level of 0.05 for the NCEP-2 and CFSR reanalysis temperature data including three elements: the daily average temperature ( Tmean), the daily maximum temperature ( Tmax) and the daily minimum temperature ( Tmin). The performance of the NCEP-2 Tmean data is poorer than those of the NCEP-2 Tmax and Tmin data for all four seasons, while the performance of CFSR Tmean is better than that of CFSR Tmax and Tmin for all four seasons. In addition, the NCEP-2 Tmax, Tmin and CFSR Tmean, Tmax data perform well in most of central and eastern China for all four seasons. The quality of the CFSR Tmean is better than that of the NCEP-2 Tmean for all four seasons. However, the NCEP-2 Tmax and Tmin data have higher credibility than that of the CFSR Tmax and Tmin data for all four seasons. Therefore, it is important to consider the reliability of the NCEP-2 and CFSR daily temperature reanalysis data in different districts and seasons when drawing conclusions from the different reanalysis data. [ABSTRACT FROM AUTHOR]

ABSTRACT In this study, the detrended fluctuation analysis ( DFA) method is used to analyse the drought and flood index ( DFI) of the past 531 years in 20 stations over east central China. It is found that the variation of the DFI exhibits long-range correlation. The long-range correlation gradually decreases from northwest to southeast over the studied region. Moreover, the recurrence times of droughts or floods also show long-range correlation. The long-range correlation in the DFI data will disappear if the DFI data are randomly shuffled, which was also found in the recurrence time of the randomly shuffled DFI series for each station. The results show that long-range correlation is an intrinsic property of drought or flood events, which could result in the long-range correlation of the corresponding recurrence times. At present, the prediction skill of droughts/floods in Asian-Australian monsoon region is relatively low, particularly in most of China. The long-range correlation of the DFI provides a theoretical basis for climate predictions of droughts/floods and may help improve model performance by amending the model configuration, in terms of addressing parameterization problems and improving atmosphere-ocean coupling. [ABSTRACT FROM AUTHOR]

ABSTRACT An abrupt change occasionally occurs when the dynamical system suddenly shifts from one stable state to a new state, which can take place in many complex systems, such as climate, ecosystem, social system, and so on. In order to detect abrupt change, this article presents a novel method - sliding transformation parameter ( STP) on the basis of skewness change and the Box-Cox transformation. Tests on model time series and 1000 simulated daily precipitation data show the ability of the present method to identify and detect abrupt change of probability density function. The applications of STP in daily precipitation data show that there is an abrupt climate change between 1979 and 1980 in the selected observational stations, which is almost the same with the result obtained by approximate entropy ( ApEn). Furthermore, it is found that the sample sizes of sliding windows have some influence on the Lambda parameter of the Box-Cox transformation, but it does not significantly affect the varying trend of the parameter and the identification of the change point in annual or interannual time scale. Comparing STP with the coefficient of skewness and kurtosis, ApEn, and some statistics approaches (e.g. percentiles and annual maxima), we find that the performance of the present method is much better than that of these methods. [ABSTRACT FROM AUTHOR]