This study used daily temperature and precipitation data from 699 weather stations in China from 1951 to 2018 to study the spatiotemporal evolution characteristics of temperature and precipitation in China， by using Mann-Kendall test， wavelet analysis， and RClimDex extreme temperature index methods. The results show that： In terms of time， the temperature in China presents an obvious increasing trend； the increase in precipitation is lagging and slow； the temperature and precipitation have multi-period changes； and there is a correlation on a large time scale. With the number of freezing days ID0 expressed by the highest temperature and abnormal warmth compared with the number of frost days FD0 and the number of warm night days TN90p expressed in the lowest temperature， the number of continuous days TX90p has a smaller change. The longest continuous precipitation days CWD and the longest drought duration CDD show a downward trend. Climate change is mainly characterized by warming and humidification. Spatially， the average temperature and precipitation both show a decreasing trend in the latitude direction. The contours move northward as a whole， which is largely affected by the topography， showing complementary spatial distribution characteristics. The number of warm days TX90p and the number of summer days SU25 are positive across the country. The number of freezing days ID0 and the number of frost days FD0 decrease in most areas. The longest continuous dry days CDD and the longest continuous humid days CWD have a certain degree of opposite spatial variation， and there is an increasing trend in extreme precipitation events. Studying the temporal and spatial evolution characteristics of temperature and precipitation is an important part of understanding and coping with the impact of climate change on the region.

With the intensification of urbanization and global warming, the problems of urban thermal environment are increasingly prominent. On the basis of the remote sensing, geographic information system, geostatistics and multiscale spatial pattern, the spatial-temporal variation characteristics of land surface temperature in urban thermal environment were quantitatively analyzed. The results are as follows: \mathrm{①} Dramatic changes in land use/land cover had occurred from 1993 to 2016 in the study area. The net increase area of construction land was 1 231.04 km², with a change rate of 295.33%. Cultivated land was occupied by construction land. \mathrm{②} The area of middle, sub-high and high temperature zones spread to the surrounding areas gradually with the Minjiang River. The area of sub-low and low temperature zones decreased markedly. \mathrm{③} From 1993 to 2016, the contribution of land surface temperature in different urban districts had the characteristics of uneven spatial and temporal distribution. Meanwhile, there was a positive contribution in the process of land surface temperature rise in Fuzhou while there was a negative contribution in Minqing and Yongtai. Forest/grassland, cultivated land, water body and wetland had a negative contribution during land surface temperature rise while construction land contributed positively. \mathrm{④} According to the multi-distance spatial cluster analysis (Ripley's K function), there was a certain scale in the aggregation and dispersion of land surface temperature, in which the aggregation range and degree of aggregation increased in the study area in 24 years.

The world has been undergoing a remarkable process of urbanization, especially in developing countries in recent years. The urbanization process has brought about great urban development and large population agglomeration, changes in production and lifestyle, and man-made disturbances such as greenhouse gas and pollution emissions. As the global urbanization process continues to advance, its impact on climate change continues to strengthen significantly. This paper mainly reviewed and summarized relevant researches from two aspects: the influence of urbanization on climate change and the mechanism of influence of urbanization on climate change. Urbanization causes regional warming and urban heat island effect, extreme events such as high temperature, heat wave and heavy rainfall increase in frequency, and also leads to increased urban flood risk. The increase of pollutant emission in the process of urbanization is the main cause of air quality deterioration. Urbanization also has an indirect impact on air quality by changing urban climate. Urbanization has an important impact on climatic factors such as relative humidity, wind speed, sunshine and cloud cover. The impacts of urbanization on climate change are mainly realized through underlying surface changes, greenhouse gas and pollution emissions, anthropogenic heat emissions and urban high heat capacity. Urbanization not only directly affects the regional/local climate, but also indirectly affects the regional/local climate by promoting global climate change. Therefore, the impact of urbanization on climate change has a global and regional multi-scale superposition effect.

Under the background of climate change, extreme weather events (e.g., heavy rainfall, heat wave, and cold damage) in China have been occurring more frequently with an increasing trend of induced meteorological disasters. Therefore, it is of great importance to carry out research on forecasting of extreme weather. This paper systematically reviewed the primary methodology of extreme weather forecast, current status in development of ensemble weather forecasting based on numerical models and their applications to forecast of extreme weather, as well as progress in approaches for correcting ensemble probabilistic forecast. Nowadays, the forecasting of extreme weather has been generally dominated by methodology using dynamical models. That is to say, the dynamical forecasting methods based on ensemble probabilistic forecast information have become prevailing in current operational extreme weather forecast worldwide. It can be clearly found that the current major directions of research and development in this field are the application of ensemble forecasts based on numerical models to forecasting of extreme weather, and its improvement through bias correction of ensemble probabilistic forecast. Based on a relatively comprehensive review in this paper, some suggestions with respect to development of extreme weather forecast in future were further given in terms of the issues of how to propose effective approaches on improving level of identification and forecasting of extreme events.

Runoff, which is a key component in the hydrological cycle, is mainly controlled by climate factors and land-surface elements in non-humid regions. The impacts of climate and vegetation changes on runoff based on Budyko hypothesis in the middle and upper reaches of the Pearl River Basin was analyzed in this article. First, the temporal trend of variables in the study area during 1981-2013 was examined by using the Mann-Kendall trend test with trend-free pre-whitening. Second, the relationship of the parameter n in Fu's equation with factors of climate and vegetation coverage was built to reveal the time-variation process of n. Finally, the effects of climatic factors and vegetation coverage on runoff were assessed by analyzing the sensitivity of runoff to each variable. It is found that average temperature (T), maximum temperature (T_max) and minimum temperature (T_min) in the study area present an increasing trend while runoff (Q), precipitation (P), wind speed (u₂) and relative humid (RH) present decreasing trend. The parameter n in Fu's equation is significantly related to both climatic factors (including precipitation (P), average temperature (T), relative humid (RH), sunshine duration (S), wind speed (u₂)) and vegetation coverage index (NDVI). In terms of sensitivity of Runoff (Q) to the variation of each climatic factors and NDVI in the middle and upper reaches of the Pearl River Basin, precipitation (P) and NDVI have the highest sensitivity, followed by other climatic factors. Additionally, the precipitation (P) reduction is the main driving factor to the decline in runoff, while vegetation coverage is another important factor. In general, climate change affects runoff not only by changing the hydrological inputs (precipitation (P) and potential evaporation (PET) but also by altering the watershed characteristics as represented by the parameter n, while the impacts of vegetation coverage on runoff are exerted mainly through the alteration of the watershed characteristics.

At present, flash drought occurs globally and regionally and causes a lot of socio-economic loss in a very short time. Therefore, flash drought has been regarded as one of the hottest issues in drought research. However, flash drought monitoring, prediction and decision-making have encountered a lot of challenges due to its multiple driven factors and complex spatio-temporal process. Aiming at this problem, this paper focused on the agricultural land in China, and analyzed the spatio-temporal distribution of three kinds of flash droughts (i.e., precipitation-deficit, high-temperature, and composite flash droughts) from 1983 to 2015. We studied the occurrences, duration, spatial distribution, temporal distribution, and trend of all three kinds of flash droughts. Our results demonstrated that, the occurrences of flash drought agricultural land in China increased year by year, among which high-temperature flash drought increased dramatically; duration of flash droughts had different trends, but the variations were relatively smooth; Northeast China was identified as a vulnerable area of flash drought, indicating more flash drought events and longer duration; flash droughts in China were found to concentrate in spring (high-temperature drought) and summer seasons (precipitation-deficit drought). This study is helpful for building new flash drought monitoring method and system, and it is also valuable for flash drought preparedness on regional scale.

Based on the observational data, the variations of Intraseasonal Oscillation (ISO) of the daily temperatures and its relationships to the high temperature in summer over the lower reaches of the Yangtze River Valley (LYRV) were studied for the period of 1979-2011. It is found that the daily temperatures over LYRV in May-August was mainly of periodic oscillations of 1525, 3060 and 6070 days, and the interannual variation of the intensity of its 3060-day oscillation had a strongly positive correlation with the number of days with daily highest temperature over 35 ℃ in July-August. Low frequency components of daily temperature in the LYRV, and the principal components of the Eastern Asian 850 hPa low frequency temperature, over a time period ranging from 1979 to 2000, were used to establish the Extended Complex Autoregressive model (ECAR) on an extended-range forecast of the 3060-day low frequency temperature over the LYRV. A 11-year independent real-time extended-range forecast was conducted on the extended-range forecast of low frequency component of the temperature over the LYRV in May-August, for the period ranging from 2001 to 2011. These experimental results show that this ECAR model, which is based on a data-driven model, has a good forecast skill at the lead time of approximately 23 days, with a forecast ability superior to the traditional autoregressive (AR) model. Hence, the development and variation of the leading 3060-day modes for the Eastern Asian 850 hPa low frequency temperatures and temporal evolutions of their relationships to low frequency components of the temperature over the LYRV in summer are very helpful in predicting the persistent high temperature over the LYRV at a 20 to 25 days lead.

Heat wave has become a severe problem over China in recent year. Based on daily air temperature data from 719 meteorological stations in China in the period of 1961-2012, the heat wave events were classified into three levels by duration: weak HW, medium heat wave and strong heat wave. This paper describes the spatial and temporal characteristics among three levels of heat wave over China during the period 1961-2013. The results showed that the number of hot days displayed a weakening trend from the 1960s to the early 1990s, followed by a strengthening trend from the late 1990s up to now. Long-term linear trends in hot days had significant regional differences, for instance, the number of hot days in southwest and southern China growing sharply than other regions; three levels heat wave occurred in different regions that weak heat wave usually occurred in the northwest and middle part of China while strong heat wave mainly happened in the southern part of China. When it came to the first day of heat wave, weak heat wave occurred earliest in China, and it happened around July 3rd while medium heat wave and strong heat wave always started on July 13th and July 14th, respectively. In general, the earliest heat wave was always found in southwest China while the last ones usually occurred in southern part and southwest China. Increasing trend of three levels heat wave were found in the entire country, except for southwest China. The trend of the last day of heat waves showed significant difference between northern and southern part of China. The last day of weak and medium heat wave occurred later in the south China, while strong heat wave happened earlier in north China.

The possible changes of extreme climates over China under 1.5 ℃ global warming scenario were investigated by using the output of CORDEX (COordinated Regional Downscaling Experiment) experiments with a regional air-sea coupled model FROALS over East Asia domain. Results indicated that compared to the baseline period of 1986-2005, warm events would significantly increase while cold events would significantly decrease over China in a 1.5 ℃ warmer world. The risks of extreme and moderate warm events would be 2.14 and 1.93 times of that in the baseline period, respectively. The risks of extreme and moderate cold events would be 0.58 and 0.63 times of that in the baseline period, respectively. Compared to other sub-regions, the increasing amplitude of extreme warm events would be higher in North China, while the decreasing amplitude of extreme cold events would be higher in Northeast China. Risks of extreme dry events would increase in Northwest China, Tibetan Plateau and Northeast China (1.13, 1.02 and 1.22 times of that in baseline period). Precipitation intensity and extreme wet events would increase significantly over most parts of China, and the increasing amplitudes extreme wet events will be higher in North China and South China (1.88 and 1.85 times of that in the baseline period). Days when people may feel uncomfortable would increase significantly in eastern China, and compared to simple extreme warm events, the increasing amplitude of extreme uncomfortable days would be larger. The absolute changes of heating degree-days would be larger than that of cooling degree-days (-258℃·d and 72℃·d, respectively) in eastern China, but the relative change of heating degree-days would be smaller than cooling degree-days (-10% and 82%, respectively).

This study investigated the changes of high temperature events during important growing period of rice (graining filling to maturity) of 2021-2050 due to climate change. Future climate scenarios were HadGEM2-ES simulation with RCP2.6 and RCP8.5 emission pathways. Relationship between high temperature and yield change was established from historical weather and field observations during 1981-2009 period. The impacts of high temperatures on China’s rice production up to 2050 were assessed by applying deduced regression models to climate scenarios. Key messages drawn from this exercise include: ①High temperature event exhibited gradual increase from 2021 to 2050 under both RCP2.6 and RCP8.5 scenarios, characterized by increased number of high temperature days (HSD), rising accumulated temperature with Tmax greater than 35 ℃ (HDD), and increased lasting days of high temperature (CHD). The HSD and HDD increased substantially in double rice cropping system of South China, single rice cropping system of Yangtze River Basin and rice area of Northeast China. ②High temperature hotspot was located near the border between Hunan and Hubei during 1961-2000, and might move towards northeast in the period of 2021-2050. ③Except the Northeast, China’s rice production suffered most from increased HDD during grain filling to maturity, indicated by significant negative and linear relationship between yield and HDD, whereas rice in Northeast China was subject to the increase of SDD during grain filling to maturity, with a significant and quadratic relationship between the yield and SDD. ④Compared to the high temperature risks during 1961-1990, climate change would increase the risks in majority of the rice area, especially in Hubei and Anhui-the central portion of Yangtze River Basin rice area, Guangdong, Guangxi and Hainan-south China double rice area, and south part of Northeast China single rice area.

Understanding of tempospatial pattern and the systimatic bias of the obeserved decadal to multidecadal variability and longterm trends of global land Surface Air Temperature (SAT) is needed for climate change studies and policymaking. This paper summarizes the state and problems of the current studies of global land SAT change, and points out the necessarty and possibility to launch a new study of global and regional SAT dataset and analyzing products. It is obvious from the overview that there exist some problems with the current three global datasets under use in global climate change research, and a major issue would be the inefficient treatments of the urbanization bias in the land SAT series. It is proposed that the Chinese global land SAT dataset developed in the China Meteorological Administration (CMA) be improved and completed, and the urbanization effect on SAT trends of global land stations be evaluated and adjusted. Based on the urbanbias adjusted SAT datasets, global and regional SAT series could be constructed and analyzed to reveal the spatial and temporal patterns of SAT variablity and change. Chinese scientists could play a more important role in the endouvour facing climate community.

With the risk of global warming, exploring the changing pattern of extreme climate events in different places is explored for disaster prevention and mitigation. The 0.5°×0.5° grid dataset of daily temperature and precipitation from China Meteorological Administration was used to defined extreme climate events based on the 16 kinds of extreme temperature and precipitation indices. Spatio-temperal variations of the extreme temperature and precipitation events were analyzed through the modified MannKendall trend detecting method across the Pearl River basin, and the significance and consistency of the observed trends were also assessed in a regional perspective. Additionally, whether the observed trends are significantly linked to the largescale climate fluctuation system was investigated. The results indicate that a trend of more extreme high temperature events and less extreme low temperature events, more short time precipitation events and less long time precipitation events has been found in the Pearl River basin over the past half century, which could, consequently, increase the drought and flood risks. It is worthwhile to note that the trends of extreme temperature events are field significant and regional consistent, while the trends of extreme precipitation events are not. Since no significant covariability has been found between the observed trends and the large-scale climate fluctuation system characterized by the multivariate ENSO index, these trends can not be seen as the inevitable outcome of largescale climate fluctuation. Instead, that may be attributed to the common effects of natural and anthropogenic climate change.