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Two skilful 2‐month‐leading hybrid downscaling prediction schemes in October for winter surface air temperature (SAT) over China are proposed in this paper. The schemes are based on the year‐to‐year increment approach and the coupled climate patterns between the winter SAT over China and its predictors. Observed North Pacific sea surface temperature (SST) from the preceding July to September, Arctic sea ice concentration (SIC) in the preceding August and winter sea level pressure (SLP) over pan Eurasia from version 2 of the Climate Forecast System (CFSv2) are selected as the predictors based on the fundamental physics. Individual‐predictor schemes (IP‐schemes), that is, SLP‐scheme, SST‐scheme, SIC‐scheme, indicate that these predictors exhibit prediction skills in different regions. Multi‐predictor scheme I (MP‐schemeI) is developed by combining three predictors. However, MP‐schemeI shows limited skill in predicting SAT over Northeast China (NECTA), due to the limited skill of CFSv2 over the extratropics. Thus, MP‐schemeII is established, in which a hybrid downscaling model for NECTA is constructed. These two MP‐schemes have comparable prediction skill over China, but MP‐schemeII outperforms MP‐schemeI over NEC. The temporal (spatial) anomaly correlation coefficient (ACC) increases from 0.23 (0.15) in MP‐schemeI to 0.36 (0.21) in MP‐schemeII, and the ratio of the same sign of anomalous years (Anomalous‐RSS) improves from 39.1% to 56.5% over NEC. For the winter SAT over China, the MP‐schemes greatly enhance the prediction compared with CFSv2 outputs and the IP‐schemes. The temporal (spatial) ACC increases from 0.29 (0.05) in CFSv2 to 0.52 (0.29) in the MP‐schemes, and the station‐average root‐mean‐square error decreases by about 48.0% compared with CFSv2. Moreover, the RSS (Anomalous‐RSS) of the winter SAT over China is 55.9% (47.4%) in CFSv2 and 64.7% (63.2%) in the MP‐schemes. This indicates that the MP‐schemes perform better in predicting anomalous winters. [ABSTRACT FROM AUTHOR]

From the perspectives of remote sensing and climatic factors like surface meteorological parameters, large‐scale atmospheric circulations, and external forcing factors (EFFs), the authors synthesize and review spatiotemporal variations of PM2.5 over North China and how climate anomalies affect autumn and winter haze variability in North China according to recent studies. This review focuses on both interannual and interdecadal timescales. It is shown that circulations play an important role in influencing haze variability. Atmospheric circulations, which would be modulated by EFFs like sea surface temperature, sea ice, and snowpack, can affect the climate variability in haze over North China via modulation of surface‐layer parameters that are closely connected with the haze phenomenon. Therefore, EFFs are deemed significant factors impacting the climate variability of haze over North China, serving as paramount precursory signals for haze prediction. Furthermore, this paper suggests potential future research directions for haze variability studies in North China on the basis of summarizing and concluding the associated processes/mechanisms on how climatic factors affect haze variability, which could provide reference for treating and forecasting in situ hazy conditions. [ABSTRACT FROM AUTHOR]

Hong Kong is a high-density sub-tropical city with 7 million people living in an urban area of just over 260 km2. Tall and closely packed buildings are the common urban morphology. How the urban geometry influences the microclimate in summer daytime is a primary planning concern. The sky view factor (SVF) has been commonly used to indicate the impact of urban geometry on air temperature differences in cities. However, only limited discussions in this aspect have been addressed for daytime course in high-rise and high-density urban environment such as Hong Kong. This paper firstly provides a comprehensive review of SVF analysis in urban climatology studies and then presents a simulation approach to investigate the role of SVF in determining summer daytime intra-urban air temperature differences in urban Hong Kong. An ArcGIS-embedded computer program is developed for calculating continuous SVF values for an entire urban environment and an SVF map is generated. The result is evaluated against meteorological data observed in field measurements. The regression analysis shows that the spatial average of SVF values has a close negative relationship with daytime intra-urban temperature differences. The study indicates that SVF is a significant factor for understanding the microthermal climate in Hong Kong's street canyons. The paper further raises discussions on the application of SVF analysis to urban planning. The study demonstrates that the SVF analysis is a useful and effective tool for planners and urban climatologists conducting studies on high-rise and high-density sub-tropical cities. The understanding can provide support for the development of planning standards and good practice. Copyright © 2010 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

In a previous study by the authors, a regional climate model (hereafter the RCM) developed to study the summer monsoon over South China (SC) and the South China Sea (SCS) has been tested and found to be able to simulate to a large extent the precipitation over this region for the months of May and June. To examine the interannual variability of the summer monsoon here, it is necessary to establish a model climatology to serve as a comparison and to reduce or even remove any systematic model biases. This paper presents the analyses of such a 10-year climatology (1991-2000). The model was initialized on 1 April and integrated up to the end of June for the ten years. The initial atmospheric conditions and lateral boundary data used in this study are from the European Centre for Medium-range Weather Forecasts '40-year' reanalyses. The RCM can reproduce well the main features of the monsoon circulation and vertical structure of the atmosphere. The RCM can simulate the intensification and northwestward displacement of the south Asian upper anticyclones from May to June, as well as the low-level moisture transport from the Bay of Bengal to SC. In the simulation, the average SCS summer monsoon onset occurs in the fourth pentad of May, which is consistent with the results from previous observational research. In addition, the RCM can reproduce the main characteristics of the onset such as the change of the low-level zonal flow from easterly to westerly as well as the rapid increase in daily precipitation. The SC and SCS precipitation anomalies have the correct sign in almost all the years. The shortcomings of the model simulation include an under-prediction of the strength of the subtropical high over the Northwest Pacific and the moisture transport from the Bay of Bengal to the Indochina Peninsula (IC) and SCS. A cold bias in surface air temperature is also observed, with the 10-year mean biases of the simulated surface air temperature over SC, SCS and IC in May and June being about −2.1 °C, −2.4 °C and −1.4 °C respectively. The 10-year mean biases of the simulated daily precipitation rate over SC, SCS and IC are about 2.0, −3.8 and 3.5 mm d−1 respectively. Copyright © 2005 Royal Meteorological Society. [ABSTRACT FROM AUTHOR]

Aerosol vertical distribution generally determines their health impacts and climate effects. By using long‐term (2007–2019) CALIPSO lidar measurements, we present a large‐scale insight into the climatology of aerosol types and their vertical structure over East Asia. Despite the low sampling frequency, comparison with MODIS aerosol optical depth (AOD) shows that the integrated CAIPSO vertical extinction can reasonably reproduce spatial patterns of the aerosol loading. With the unique advantage of active detection, CALIPSO reveals an obvious enhancement of the night‐time AOD due to worse diffusion conditions. Moreover, long‐range transport of different aerosols including dust, polluted dust, and smoke has substantial contribution to the aerosol loading over East Asia. Pure dust particles are mainly concentrated over the deserts with notable dust transport belts (dust AOD > 0.2 at 532 nm) along downwind regions during winter and spring. By contrast, polluted dust is prevalent over the downstream eastern China with much higher AODs throughout the year. In particular, AOD of polluted dust (~0.5) is higher in winter rather than in spring, which is consistent with their seasonal occurrences. Smoke aerosols usually appear in the night‐time over southern China. The top heights of aerosols are around 4–6 km, with dust and smoke having higher extinction at upstream regions. The climatology of these aerosol types and vertical distribution can provide a significant constraint for associated studies of air quality and climate effects. [ABSTRACT FROM AUTHOR]

Performance of six models in the Coupled Model Intercomparison Project phase 5 (CMIP5) and their new versions in CMIP phase 6 (CMIP6) in representing the climatological (1976–2005) precipitation extremes over China were evaluated based on five precipitation indices. Improvements are found in CMIP6 models in simulating the climatology of all five indices, in which GFDL‐CM4 and GFDL‐ESM4 show significant improvement. Dry biases over South China (SC) are reduced in five CMIP6 models (BCC‐CSM, CanESM, GFDL‐CM, GFDL‐ESM, and IPSL‐CM), with the largest decreased root‐mean‐square error (RMSE) of 59.2% in GFDL‐CM. The reduced dry biases in CMIP6 can be attributed to more moisture transported into SC from the southern boundary and dynamic processes of the atmosphere except in BCC‐CSM, where the increased evaporation dominates. Additionally, increased heavy precipitation events (>20 mm·day−1) are produced over SC in CMIP6 models. Wet biases over West China (WC) are also reduced. with the largest reduced RMSE of 46.8% in GFDL‐CM, which are related to the reduced precipitation frequency (more than 40%) and weakened precipitation intensity. In addition, the CMIP6 models show a higher skill in simulating the frequency distribution of daily precipitation intensity. More heavy precipitation over SC and Northeast China, and fewer weak precipitation (<20 mm·day−1) over WC can be reasonably reproduced. Although the CMIP6 models have obviously improved in simulating total precipitation on wet days, wet days (WD), simple daily intensity index (SDII), and extreme precipitation amount (R95T), the bias still exists in simulating consecutive dry days (CDD). [ABSTRACT FROM AUTHOR]

Climate warming generally is expected to increase drought, but arguments about China's past drought trends persist. PDSIARTS, a revised self‐calibrating Palmer Drought Severity Index, was computed for China over 1982–2016 using satellite leaf‐area indices combined with monthly climate data interpolated from 2000 high‐density stations. Drought climatology was analysed against climate factors. The results show that temperature has increased at a rate of 0.38°C per decade (p <.001) in China in the past 35 years (1982–2016). However, over the same interval, China and the northern China region became wetter. PDSIARTS increased at a rate of 0.03 yr−1 (p <.001) across China during 1982–2016. The analogous increase of PDSIARTS in northern China was 0.05 yr−1 (p <.001). In China, the 5‐year interval from 2012 to 2016 was the wettest 5 years in the 35‐year interval. This arises from the coupled effects of decreased potential evaporation (Ep) and increased precipitation (Pr). The implication is that temperature increase does not necessarily indicate increased drought. A potential complication is that the 2015/16 El Niño event induced the highest Pr in southern China for 1982–2016, and northern China still had plentiful Pr in 2015/16, which further contributed to the wettest 2‐years (2015/16) during the past 35 years in the whole of China. This study highlights the joint impacts of Pr and Ep on the dry/wet changes and the possibility of extremely wet events in the warming future. [ABSTRACT FROM AUTHOR]

The selection of general circulation models (GCMs) with high capability to represent the past and likely future climate for a specific geographical location is a crucial step to assess impacts of climate change on different sectors. This study included pool of 105 and 78 GCMs for representative concentration pathways (RCPs) 4.5 and 8.5, respectively, from Coupled Model Intercomparison Project—Phase 5, applied an advanced envelope‐based selection approach to select representative GCMs for the Koshi river basin in China and Nepal at short‐term (2016–2045), mid‐century (2036–2065) and end‐of‐century (2071–2100) periods, and developed range of possible future precipitation and temperature scenarios with high resolution downscaled data (10 × 10 km2), which is the novelty contribution of the study. Considering RCP4.5, average annual precipitation is expected to increase by 0–16%, 4–23% and 4–24% in the short‐term, mid‐century and end‐of‐century periods, respectively. Using RCP8.5, equivalent predictions are 6–20%, 6–36% and 13–49% in the short‐term, mid‐century and end‐of‐century periods, respectively. Average annual temperature is expected to increase, but with higher increases during winter than in the monsoon period. Considering RCP4.5, average annual temperature is expected to increase by 1–1.4°C, 1.3–1.9°C and 1.6–2.8°C in the short‐term, mid‐century and end‐of‐century periods, respectively. Similarly, using RCP8.5, equivalent predictions are 1–1.6°C, 1.8–2.9°C and 3.1–5.6°C in the short‐term, mid‐century and end‐of‐century periods, respectively. The ensemble mean of absolute change in average precipitation and temperature projects that High Himalaya and Tibet regions are more sensitive to climate change considering precipitation and temperature, respectively. The results also suggest that GCMs selection for a catchment varies with climate scenarios and specific future time periods. [ABSTRACT FROM AUTHOR]

The extreme precipitation regimes have been changing as the climate system has warmed. Investigating the non‐stationarity and better estimating the changes of the extreme precipitation are valuable for informing policy decisions. In this study, two precipitation indices are employed to describe the extreme events, including maximum 5‐day precipitation amount (RX5day) and the number of very heavy precipitation days (R20). The generalized additive models for location, scale and shape (GAMLSS) is employed to characterize non‐stationarities in extreme precipitation events and related climate indices in 13 stations in the Hanjiang River basin (HJRB). Three models including stationary model without change (M0), non‐stationary models over time (M1) and non‐stationarity models with large‐scale climate indices (M2) as predictors, respectively, are considered to analyse occurrence rates of extreme precipitation. The optimal model and the significant predictors were selected by the Akaike information criterion (AIC). To investigate the main predictors at regional scale, the homogeneous subregions for precipitation extremes are identified by clustering analysis. Results indicate that: (a) the non‐stationarities of RX5day series and R20 series at all stations are identified in the HJRB; (b) extreme precipitation behaviour is significantly influenced by climate indices and non‐stationary model 2 to describe the changes of extreme precipitation is better than non‐stationary model 1, indicating the impact of large‐scale climate forcing on the changes of extreme precipitation regimes; (c) the HJRB can be categorized into three homogenous regions. The optimal distributions and the main predictors of extreme precipitation events in most stations of each subregion are basically the same; (d) the dominated climate indices influencing the extreme precipitation events are different in different regions and have regional patterns. The results highlight the modelling of extreme precipitation events under non‐stationarity conditions and provide information for developing strategies of mitigation and adaptation to climate change impacts on extreme precipitation. [ABSTRACT FROM AUTHOR]

Climate model response (M) and greenhouse gas emissions (S) uncertainties are consistently estimated as spreads of multi‐model and multi‐scenario climate change projections. There has been less agreement in estimating internal climate variability (V). In recent years, an initial condition ensemble (ICE) of a climate model has been developed to study V. ICE is simulated by running a climate model using an identical climate forcing but different initial conditions. Inter‐member differences of an ICE manifestly represent V. However, ICE has been barely used to investigate relative importance of climate change uncertainties. Accordingly, this study proposes a method of using ICEs, without assuming V as constant, for investigating the relative importance of climate change uncertainties and its temporal–spatial variation. Prior to investigating temporal–spatial variation in China, V estimated using ICE was compared to that using multi‐model individual time series at national scale. Results show that V using ICE is qualitatively similar to that using multi‐model individual time series for temperature. However, V is not constant for average and extreme precipitations. V and M dominate before 2050s especially for precipitation. S is dominant in the late 21st century especially for temperature. Mean temperature change is projected to be 30–70% greater than its uncertainty until 2050s, while uncertainty becomes 10–40% greater than the change in the late 21st century. Precipitation change uncertainty overwhelms its change by 70–150% throughout 21st century. Cold regions (e.g., northern China and Qinghai‐Tibetan Plateau) tend to have greater temperature change uncertainties. In dry regions (e.g., northwest China), all three uncertainties tend to be great for changes in average and extreme precipitations. This study emphasizes the importance of considering climate change uncertainty in impact studies, especially taking into account that V is irreducible in the future. Using ICEs without assuming V as constant is an appropriate approach to study climate change uncertainty. Using initial condition ensembles without assuming internal variability as constant is an appropriate approach to study climate change uncertainties. Changes in internal variabilities of average and extreme precipitations are significant. Temperature change is greater than its uncertainty first, then the other way round. Precipitation change is less than its uncertainty in this century. Cold regions have great temperature change uncertainties, while dry regions have great precipitation change uncertainties. [ABSTRACT FROM AUTHOR]

Based on 55 years (from 1961 to 2015) of ground‐based hailstorm and atmospheric sounding observations, the climatological characteristics of hailstorms in China, along with potential influencing factors, were analysed. We found that hailstorms exhibit large spatial and temporal variability, with the peak occurring in spring in south China, and in summer in north China. The Tibetan Plateau (TTP) is a region with high hailstorm frequency. Overall, the frequency of hailstorms has decreased over the 55 years, although there was an increase from 1961 to 1985. In terms of diurnal variability, while hailstorm frequency peaks in the afternoon in most of China, it peaks during the night or early morning in southwest China. The magnitude of the peak increased during the 1960s and 1970s, and then decreased afterwards. Hailstorms are negatively correlated with daily mean temperature but are positively correlated with diurnal temperature range, convective available potential energy (CAPE) and totals–totals index. The increase of hailstorms during the period from 1961 to 1985 is mainly correlated with the increase in CAPE over the TTP region, while the decrease of hailstorms afterwards is correlated with all four convective parameters. The finding has significant implications for the potential impact of increasing greenhouse gases on climatological changes in hailstorm. [ABSTRACT FROM AUTHOR]

An extreme high‐temperature (HT) event occurred in China in the summer of 2016. Northwestern and southern China experienced more than 20 HT days with a daily maximum temperature (DMT) greater than 35 °C. Compared with the average local climatology, southern China experienced 10–15 more HT days with temperatures greater than 35 °C and 5–10 more HT days with temperatures greater than 38 °C. The extreme HT event in 2016 in southern China began on July 20, responding to the end of the Chinese Mei‐yu in the region. The HT event lasted for approximately 2 weeks until August 2. The western Pacific subtropical high (WPSH) in the summer of 2016 was much stronger and more westwards than normal owing to a strong El Niño event. During the HT period, the WPSH shifted further westwards. Its western boundary reached 106°E, and it covered most parts of southern China. It was more westwards than its average position during the 1981–2010 reference period and the previous Mei‐yu period. The high experienced a notable shift northwards covering all regions south of the Yangtze River valley. The South Asian high moved eastwards covering the East Asian continent. Thus, the upwards flow at the lower tropospheric levels over southern China in the previous period was replaced by a much stronger downwards flow. The Chinese Mei‐yu ended and southern China therefore entered a dry and hot period. During the HT period, the Somalia cross‐equatorial flow (CEF), the Bay of Bengal (BOB) CEF, and the westerly flow over the tropical Indian Ocean weakened clearly. Lag correlation analyses indicate that the WPSH had a close relationship with zonal wind over the ocean. This suggests that the westwards extension of the subtropical high corresponding to the HT event was caused mainly by the weakening of the CEFs and the westerly flow over the tropical Indian Ocean. Case of 2017 HT event and composed circulations of 28 HT cases since 1981 also supported this result. In August 2016, the MJO was much active over the western Pacific, which weakened the WPSH and finally ended the extreme HT event. [ABSTRACT FROM AUTHOR]

ABSTRACT The science that humans are the cause of global warming, and that the associated climate change would lead to serious changes in climate extreme events, food production, freshwater resources, biodiversity, human mortality, etc. is unequivocal. After several political negotiations, a 2 °C warming has been considered to be the benchmark for such damaging changes. However, an increasing amount of scientific research indicates that higher levels of warming are increasingly likely. What would the world be like if such higher levels of warming occurred? This study aims to provide information for better politically driven mitigation through an investigation of the changes in temperature- and precipitation-based extreme indices using CMIP5 (coupled model intercomparison project phase 5) simulations of a warming of 1, 2, and 3 °C in China. Warming simulations show more dramatic effects in China compared with the global average. In general, the results show relatively small change signals in climate extreme events in China at 1 °C, larger anomalies at 2 °C, and stronger and more extended anomalies at 3 °C. Changes in the studied temperature indices indicate that warm events would be more frequent and stronger in the future, and that cold events would be reduced and weakened. For changes in the precipitation indices, extreme precipitation generally increases faster than total wet-day precipitation, and China will experience more intensified extreme precipitation events. Furthermore, the risk of flooding is projected to increase, and the dry conditions over northern China are projected to be mitigated. In certain regions, particularly Southwest China, the risks of both drought and flood events would likely increase despite the decreased total precipitation in the future. Uncertainties mainly derived from inter-model and scenario variabilities are attached to these projections, but a high model agreement can be generally observed in the likelihood of these extreme changes. [ABSTRACT FROM AUTHOR]

ABSTRACT Extreme climate events over China at the end of the 21st century (2080-2099) are investigated using the regional climate model RegCM4. Model performance is validated through comparison between observations and simulations over the period 1985-2005. The results show that RegCM4 can satisfactorily reproduce the spatial distribution of extreme climate events over China. The model simulates temperature extremes more accurately than precipitation. Under the RCP8.5 (high emission) scenario, the number of frost days decreases, and both the heat wave duration index and the growing season length increase dramatically towards the end of the 21st century. Changes in extreme temperature become increasingly pronounced from South to North China, with the most significant changes occurring on the Tibetan Plateau ( TP). The proportion of heavy precipitation generally increases, except on the southern TP. The number of very heavy precipitation days increases by 25-50% in Northwest and East China. In winter, the number of consecutive dry days ( CDD) decreases in North China and increases in South China. The greatest increases in CDD are found in June, July and August ( JJA) in Southwest China. In a future that follows this scenario, drought events may be aggravated in Southwest China, and decrease in North China. In contrast, when repeating these projections under the assumption of the RCP4.5 scenario for emissions, the frequency of extreme climate events is reduced. These results suggest that reductions in greenhouse gas emissions may mitigate the effects of climate change over the coming decades. [ABSTRACT FROM AUTHOR]

The article discusses the reconstruction of climate using a variety of historical documents in China. It is stated that there were 14 published temperature series by different studies that were examined for coherence and mutual consistency. The analyses revealed that for the individual time series on the 10-year time scales, 57 of the 91 correlation coefficients attain the implication level of 99%. Furthermore, consistency also show when equating the reconstructions with other available natural climate change indicators.

The article offers information on the climactic teleconnections of summer monsoon streamflow to the Three Gorges Dam (TGD) at the Yangtze River Basin in China. From the period of 1882 to 2003, the summer monsoon inflow was analyzed at Yichang hydrological station (YHS). Statistical analysis was utilized to create a probative model for summer streamflow, followed by linear correlation maps using three-month ahead climate fields to determine the regions that exhibit climactic influences with streamflow at Yichang. The analysis shows regions in the eastern India and western Pacific oceans influences the YHS streamflow. It is inferred that an accurate climactic prediction can provide guidance for water management in the Yangtze River Basin.

The article presents a study on El Niño Southern Oscillation (ENSO) and the onset date of South China Sea summer monsoon (SCSSM). It mentions that the monsoon onset date (MOD) is determined by the change in direction of the zonal winds in the South China Sea from going towards the east to west in two consecutive pentads. It discusses that the ENSO is affected by the ocean heat content (OHC). The study found that the monsoon starts late in a period of warm ENSO. It also mentions that winds in the east of the Philippines affect the onset of the MOD. The study also relates the effects of cold subsurface water in the western North Pacific with SCSSM.