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Global warming has accentuated the effects of extreme heat on health. Health insurance, functioning as a risk management tool, has the potential to alleviate these impacts. Consequently, this paper investigates the correlation between extreme heat events and the demand for health insurance in China. Using data from the China Health and Nutrition Survey, we have observed a substantial increase in the likelihood of residents purchasing health insurance during extreme heat events. To be specific, for every extra day of extreme heat events annually, there is a 0.3% increase in the probability of purchasing health insurance. This effect is not uniform across different demographic groups. It is particularly pronounced among middle-aged and elderly individuals, rural residents, those with lower educational levels, higher income brackets, and individuals residing in underprivileged areas with limited access to green spaces and healthcare facilities. Furthermore, our study indicates that the increased frequency of extreme heat events not only impacts individuals' physical health but also triggers negative emotions, which in turn drive risk-averse behavior related to health insurance purchases. These findings carry substantial policy implications for mitigating the economic consequences of climate change. • Extreme heat increases the likelihood of residents purchasing health insurance. • Each additional day of extreme heat boosts health insurance purchases by 0.3%. • This effect varies among different populations. • Mechanistic analyses indicate extreme heat impacting physical and mental health. [ABSTRACT FROM AUTHOR]

Abstract Vegetation-atmosphere interaction is thought to have significant impacts on modulating regional climate. The main purpose of this paper is to reveal the impact of vegetation change on regional climate simulation over Eastern China. Therefore, we have performed three 30-year simulations from 1982 to 2011 with the WRF model coupled with the Noah land surface model as follows: a control experiment with climatology green vegetation fraction (GVF) (CTL), a sensitive experiment with WRF default climatology GVF (CLMV), and a sensitive experiment with dynamic vegetation (DYNV). The climatology difference between the CTL and CLMV experiment points out that during 1982–2011 over Eastern China, the increase of vegetation causes the decrease of temperature; on the other hand, the increase of temperature often corresponds to the reduction of vegetation. In the meantime, the increase/decrease in GVF is quite consistent with the simulated increase/decrease in precipitation, which is caused by both circulations and water vapor distributions at lower levels. The difference in the temperature trend between the CTL and DYNV experiment is almost opposite to that between the DYNV and GVF trend, which means that the decreased/increased vegetation will increase/decrease the surface air temperature. However, the trend of precipitation variation shows no clear relation with that of GVF. In response to GVF change, the results show that in the vegetation-climate feedback the moisture feedback is dominant over the radiation and momentum feedback. Significant decrease in GVF and LAI is detected across the East China, which leads to the decrease in the latent heat flux and increase in the sensible heat flux. For extremes and heat waves, it is shown that DYNV leads to better stimulation on the maximum of daily maximum temperature (TXx), warm spell days (WSDI), heat wave days (HWNY) and the averaged heat wave duration (HWDU) than CTL does over most of the Southeast China, although it may introduce some new biases in the Eastern coastal regions as well as the Northeastern China. Highlights • Dynamic green vegetation fraction (GVF) data was used for simulation. • Studied the effect of vegetation variation on climate extremes. • The results are also compared with actual observations. [ABSTRACT FROM AUTHOR]

Over the past hundred years, remarkable changes occurred around global climate. Two significant characteristics of climate changes are temperature mutation and warming hiatus, which led to a variety of extreme climate events of all scales, such as in the north of China. Therefore, revealing the features and rules of temperature mutation and warming hiatus in northern China could provide some basis to deeply understand climate changes, prevent and mitigate disasters as well as improve ecological environment in northern China and in the whole world. Based on the seasonal (monthly) average minimum temperature, average temperature and average maximum temperature data at 357 meteorological stations in northern China and surrounding areas during 1951-2014, this paper analyzes the spatio- temporal variation of three types of seasonal temperature (the average minimum temperature, average temperature and average maximum temperature) mutation and warming hiatus over northern China by using Mann-Kendall test, slide t test and other methods. The results indicate: according to the average minimum temperature, average temperature and average maximum temperature, the abrupt change of temperature and warming hiatus became late and the cycle from abrupt change to warming hiatus became short as the latitude reduces. In spring and winter, the temperature mutation and warming hiatus in the Northeast China were the earliest (1970s- 1980s, 1993- 2002), in North China were the second and in the Northwest China were the latest (1990s-2000s, 1996- 2010). While in summer and autumn, the temperature mutation and warming hiatus in North China were the earliest, in Northeast China were the second and in Northwest China were the latest (1990s- 2010s). There was little distinction among areas in warming hiatus year. Meteorological stations with no mutation and no hiatus were centralized in mountainous regions, high- latitude areas and southern part of North China Plain where the surrounding areas were relatively late in mutation and warming hiatus. The mutations and warming hiatus of same kinds of temperature became late in the order of winter (1981- 1990), spring, autumn and summer (1994-2008) and winter (1995-2008), autumn, summer and spring (1998-2010). The cycle from temperature mutated to warming hiatus became short in the sequence of winter, spring, autumn and summer, during which the average minimum temperature period was the longest in winter (9- 24 a). The average minimum temperature mutated earliest in spring, summer and winter, which occurred during 1972-1999, 1987-1999 and 1971-2000, the average temperature was the second to mutate, while the average maximum temperature was the latest to mutate which occurred during 1975- 2008, 1994- 2008, 1972- 2006. However, in autumn, the mutation of the average temperature was the earliest (1982-2001), the mutation of the lowest average temperature was the second, and the mutation of the highest average temperature was the latest (1987-2001). The warming hiatus of the average minimum temperature, average temperature and average maximum temperature in spring and summer became late successively, which were during 1994- 2008, 1997-2008 and 1997-2010, respectively, while the sequence was opposite in autumn and winter. The lengths of periods from temperature mutation to warming hiatus in each season in order were that of the average minimum temperature (9-18 a), that of the average temperature and that of the average maximum temperature (5- 12 a). In particular, the three kinds of temperatures all mutated earliest in the southern of North China in summer, which is inconsistent with the rule in the whole area that the mutation became late as latitude decreases, and the temperature at most stations in this area did not mutate, which is also inconsistent with the general rule in the whole area. [ABSTRACT FROM AUTHOR]

This paper reviews urban heat (UrHT) challenges following the SBAR (situation, background, assessment and recommendation) framework. The results indicate that heatwaves become more frequent, lasting and intense, especially after 1990s. Above 1960s level, heatwaves across China doubled in both magnitude and frequency by 2018. Jianghuai and Southern China underwent the largest magnitude and most widespread increases. Under 1.5 °C warming limit, the average heatwave days and duration across China will increase by 10.8 days and 3.9 days. Drought–heatwave co–occurrence is increasingly frequent at 7–11%/decade (from 1961 to 2018) and the co–occurrence leads to more intense heatwaves. UHIs are a common issue for almost all Chinese cities and UHIs have been aggravating annually. Daytime UHIs peak in summer, indicating the synergies with heatwaves. The synergies are prominent in southeastern cities for strong summer daytime UHIs in eastern cities and intense heatwaves in southern regions. UrHTs have not been recognised and there are no dedicated/mandatory plans. Mega–challenges of climate change, rapid urbanisation, carbon– and labour–intensive economic growth and demographic changes can potentially lock China into UrHT challenges. Addressing UrHT challenges is urgent in China not only for environmental, ecosystem, social and health consequences, but also for economic impacts relevant to labour, capital, and goods or services. Efforts are suggested in technical improvement, policy formulation, social participation, economic investment and co-benefit approach recognition. Overall, this paper provides a comprehensive understanding of heat–related challenges in China and can guide the creation of cool cities and communities in practice. [Display omitted] • Almost all cities in China are under the impacts of urban heat islands. • Heatwaves and urban heat islands can synergise, especially in Southeastern China. • China has not well recognised urban heat challenges and no dedicated plan. • UrHT affects economic productivity by affecting outputs, labour, capital and goods. • UrHT may drive population migrating out the extreme heat areas. [ABSTRACT FROM AUTHOR]

Quantifying the urbanization effect on trends in climate extremes is important both for detection and attribution studies and for human adaptation; however, a fundamental problem is how to accurately estimate a trend and its statistical significance, especially for non-Gaussian and serially dependent data. In this paper, the choice of trend estimation and significance testing method is suggested as important for these kinds of studies, as illustrated by quantifying the urbanization effect on trends in seven hot-extreme indices for the megacity of Shanghai during 1961-2013. Both linear and nonlinear trend estimation methods were used. The trends and corresponding statistical significances were estimated by taking into account potential non-Gaussian and serial dependence in the extreme indices. A new method based on adaptive surrogate data is proposed to test the statistical significance of the ensemble empirical mode decomposition (EEMD) nonlinear trend. The urbanization contribution was found to be approximately 34 % (43 %) for the trend in the non-Gaussian distributed heat wave index based on nonparametric linear trend (EEMD nonlinear trend) estimation. For some of the other six hot-extreme indices analyzed, the urbanization contributions estimated based on linear and nonlinear trends varied greatly, with as much as a twofold difference between them. For the linear trend estimation itself, the ordinary least squares fit can give a substantially biased estimation of the urbanization contribution for some of the non-Gaussian extreme indices. [ABSTRACT FROM AUTHOR]