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Moisture sources and transport processes play a critical part in hydrological cycle and determine regional precipitation. This paper utilizes the Water Accounting Model‐2layers (WAM‐2layers) and the ERA5 reanalysis data to track the sources of precipitation over the Pearl River Basin (PRB). The contribution of external moisture and the role of local recycling are investigated. The results show that during the period from 1980 to 2020, oceanic sources including the western North Pacific and Indian Oceans serve as the primary moisture sources of precipitation over the PRB. The contributions to total seasonal precipitation are respectively 62.57% in MAM, 54.79% in JJA, 43.70% in SON and 60.88% in DJF. By contrast, the contribution of local recycling is generally below 5.50%. In the dry years of 1994, 1997 and 2001, the contribution of terrestrial sources is about 19.22%; in the wet years of 1989, 2009 and 2011, the contribution is about 16.31%. The summer precipitation anomalies are mainly attributable to moisture anomalies from the Equatorial Indian Ocean in the wet years and from Southeast Asia in the dry years. Furthermore, vertically integrated moisture flux anomalies over the boundaries of the PRB are generally the result of anomalous wind rather than anomalous moisture. In the wet years, low‐pressure systems induce strong cyclonic moisture transports, increasing the PRB precipitation. In the dry years, high‐pressure anomalies over the PRB block the moisture transports from the Indian Ocean and western North Pacific. [ABSTRACT FROM AUTHOR]

The uneven changes in potential evapotranspiration (PET) in response to temperature rise are called the 'evapotranspiration paradox' phenomenon, which is expected to intensify further under a warming climate. In this paper, we explored the spatial–temporal changes in the future 'evapotranspiration paradox' phenomenon over China and its 10 major river sub‐regions under different climate change scenarios. Thus, this paper uses four global climate model outputs under seven shared socioeconomic pathway‐based scenarios (SSP1‐1.9, SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, SSP4‐3.4, SSP4‐6.0 and SSP5‐8.5) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Considering the latest IPCC's 6th Assessment Report (AR6), this research emphasizes the 2021–2040 (near‐term), 2041–2060 (mid‐term) and 2081–2100 (long‐term) periods to anticipate the 'evapotranspiration paradox' phenomenon. In this study, PET is estimated based on the modified Penman–Monteith (P‐M) method (considering CO2). Furthermore, the paradox phenomenon in this study is defined considering two pivotal conditions: the surface temperature increases but the evaporation decreases (Type I), and the temperature decreases but the evaporation still tends to increase (Type II). The results show that there were only Type I 'evapotranspiration paradoxes' that existed in the historical period, which were dominant especially before the 1990s. Nearly 50% of the areas experienced the Type I 'evapotranspiration paradox' phenomenon that occurred during 1975–1994 and 1995–2014. Spatially, it covered 100% of the area of the Southeast River (SER) and the Liaohe River (LR) during 1975–1994 and the area of the SER, the HAR, the HHR and the LR during 1995–2014. In the future, the interdecadal growth rate of PET in China is projected to be the highest under the SSP5‐8.5 and the lowest under the SSP3‐7.0 with spatial variation. Importantly, the largest areas of approximately 36% and 45% with the Type I phenomenon are inclined to occur under the SSP1‐1.9 and SSP4‐6.0, respectively, over the long‐term period (2081–2100). The area with the Type I phenomenon will be less than 20% in the near‐term, and it is less than 12% in the mid‐term period. For the Type II evapotranspiration paradox, the uppermost 45% of the area is expected to experience the Type II phenomenon under SSP1‐1.9 during the mid‐term period, while it is 30% under SSP1‐2.6 during the long‐term period. However, this study's findings provide the scientific basis for formulating adaptation and mitigation strategies to combat 'evapotranspiration paradox'‐related extremes at regional scales. [ABSTRACT FROM AUTHOR]

This paper utilizes reanalysis data and statistical methods to elucidate the physical mechanisms of intraseasonal variations of precipitation over southern China during the boreal winter, as well as the two primary factors influencing these variations. Anomalous southerly (northerly) wind over southern China will result in the strong moisture convergence (divergence) in enhanced (suppressed) scenarios, and the stronger positive (negative) horizontal advection of absolute vorticity at upper troposphere induces the anomalous ascending (descending) motion in enhanced (suppressed) scenarios. The Madden–Julian oscillation (MJO) in the tropics and a wave train over the North Atlantic and Eurasia with four activity centres in the mid–high latitudes (referred as NAE‐wave train) are the two primary factors that modulate the intraseasoanl precipitation over southern China. The intraseasonal circulation induced by MJO and NAE‐wave train exhibits distinct features, resulting in different physical mechanisms of their influences. The moisture flux divergence anomalies over southern China associated with MJO mainly stem from the meridional moisture convergence, whereas the meridional moisture advection is the first contributor in the influence of NAE‐wave train. The anomalous vorticity advection at upper troposphere associated with MJO is primarily influenced by the meridional advection, but that associated with NAE‐wave train is dominated by the zonal advection. Anomalous precipitation induced by MJO is concentrated mainly over South China, while that caused by NAE‐wave train is primarily located in the mid–lower Yangtze River basin. Therefore, different phases locking of MJO and NAE‐wave train could lead to various precipitation anomalies over southern China. [ABSTRACT FROM AUTHOR]

General circulation models (GCMs) are developed to simulate the past climate and generate future climate predictions. In the context of global warming, their important roles in identifying possible solutions to water resources planning/management are recognized by the world. However, in actual and regional implementation, due to many factors like initial and boundary conditions, parameters and model structures and so forth, there are great variabilities and uncertainties across the future climate projections of GCMs outputs, which has attracted criticism from water resources planners. Thus, the GCMs usually must be evaluated for assessing their performances in simulating the historical observations. Currently, there are many different conclusions and opinions as to whether the optimal individual model is more advantageous or whether the combined consideration of MME works better. The purpose of this paper is to compare the advantages and disadvantages between the selection of the optimal single GCM and multi‐model ensemble (MME) based on one of the more objective selection methods called TOPSIS. The results show that the performances of GCMs in simulating precipitation and temperature in different climate subregions over China are not identical. For CMIP6 GCMs simulations, the optimal precipitation GCM is CMCC‐CM2‐SR5 for EC, MIROC6 for SC, SWC and NWC, CESM2‐WACCM for NC and NEC, and FGOALS‐g3 for QTP. As for temperature, only NESM3 and BCC‐ESM1 are the optimal GCM for QTP and NWC, respectively; in other subregions, MME is better than single GCM. In general, a simple arithmetic averaging approach employed to generate the MME model is not superior to the optimal GCM, although the error metric with the observed data is reduced, at the cost of a severe compression of the interannual variability. [ABSTRACT FROM AUTHOR]

Drought seriously endangers the development of agro‐pastoral ecotone in arid and semi‐arid regions. However, drought evolution characteristics of semi‐arid sandy grassland and its drivers are not well understood in the complex climate change context. Thus, in this paper, the revised standardized precipitation evaporation indices on one‐month (SPEI‐1) and growing‐season (SPEI‐6) scale, flash drought and dry spells (DS) were calculated as drought indicators, and the temporal variabilities and meteorological drivers of drought in Horqin Sandy Land during the growing period (April–September) from 2007 to 2021 were studied. The results showed that SPEI‐1 and SPEI‐6 exhibited negative trends and periodic change. This drought aggravation was manifested in increased grades and numbers of drought months. Flash droughts can occur every month during the growing season. And its occurrence was not only positively correlated with all drought events in frequency but also synchronized in time. The increased number of DS showed an asymmetrical shift towards 7‐day‐and‐longer DS. SPEI sensitivity to precipitation (P) was noticeably higher than to atmospheric evapotranspiration demand (AED) at both monthly and growing season scales. SPEI sensitivity to AED was modulated by P amounts, which was higher during low P period. Apart from the amounts of P and AED, their temporal variability also strongly determined how they affect the drought severity. P was also the main driver for flash drought and long DS, but AED with larger amount and higher variability is the reason for their aggravation. Our results demonstrate the complexity of drought intensification in semi‐arid sandy grasslands and highlight the important role of AED anomalies in it. [ABSTRACT FROM AUTHOR]

This paper analyses the intraseasonal evolution of the winter haze pollution over the Beijing‐Tianjin‐Hebei (BTH) region in China between 1979 and 2013, and the influences of the atmospheric intraseasonal oscillations (ISOs) in mid–high latitudes. Two significant ISO signals of 10–20‐day and 20–40‐day are identified from the winter haze pollution over the BTH region, and their superposition notably contributes to the occurrence of haze pollution. On the 10–20‐day timescale, the haze pollution is intimately associated with a mid–high‐level southeastward propagating wave train that originates from the North Atlantic. With the wave evolution, an anomalous anticyclone reaches the Korean Peninsula–Japan, leading to strong descents and anomalous southerly winds over the BTH region, favourable for haze pollution. On the 20–40‐day timescale, the atmospheric wave train originates from the eastern North America, propagates eastward to the northwestern Lake Baikal, and then turns southward to the northeastern China–Japan. The peak stage of the haze pollution is related to the resultant zonal dipole, with the anomalous cyclone over the northwestern Lake Baikal and the anomalous anticyclone over the northeastern China–Japan. The dominance of the mid–upper tropospheric anticyclone over the BTH region suppresses the development of convection, thus hindering the vertical diffusion and wet deposition of pollutants. Moreover, the anomalous westerly winds between the dipole weaken the incursion of cold mass from higher latitudes, thus further contributing to the peak of haze pollution. [ABSTRACT FROM AUTHOR]

This paper describes the construction of a 0.25° × 0.25° daily soil temperature (TS) data set at six soil depths (i.e., 0, 5, 10, 15, 20, and 40 cm) based on a homogenized TS at more than 2,000 meteorological stations across mainland of China for 1960–2017. First, at each station, the observed TS is split into a daily climatology of 1981–2010 and a daily anomaly for 1960–2017. Then, they are respectively interpolated to 0.25° × 0.25° horizontal resolution by using the thin‐plain spline and the angular distance weight interpolation methods. Finally, a gridded multilayer TS data set is constructed from the sum of the above two gridded products. Latitude, longitude, and high‐resolution digital elevation models are explicitly incorporated into the interpolation processes. Selectivity tests indicate that the optimal interpolation distance for the TS anomaly field is 500 km, where the gridded data set may have maximum coverage of the entire Chinese mainland. The results show that the gridded TS climatology is slightly lower than the station observations. The differences between gridded and the station‐based TS varies ±3°C, with relatively large in SW and TIBET. For the gridded anomaly fields, the cross‐validation shows that the mean absolute errors between gridded and station‐based TS are generally less than 0.6°C in the YZ, SW, and SC, but they are above 1.0°C in the NW and TIBET due to the sparse station distribution and large topographic relief. From 1961 to 2016, the seasonal variability of the gridded TS is generally consistent with that of the surface air temperature. The gridded TS over the majority of land displays significantly positive trends in both summer and winter (p > 95%), indicating an overall warming land surface in China. The long‐term gridded TS data not only preserves the spatial features represented in the station observations, but also provides a spatio‐temporal continuous long‐term product of mainland China. Thus, it may be used in future studies, for example, evaluation of land surface model simulations, validation of satellite retrievals, land‐atmosphere interactions, and climate change. [ABSTRACT FROM AUTHOR]

Increasing variability of precipitation induced by climate change raises uncertainties for ecosystem services and agricultural production in the Poyang Lake Basin (PLB), China. Reanalysis precipitation datasets (RPDs) are attractive alternatives for monitoring the hydro‐climate cycles. However, their applicability in China has mainly been evaluated at the national scale, while less attention has been paid to the PLB or southeastern China. Here, we apply various metrics to evaluate the magnitude and spatial similarity of annual, monthly, different intensities and the spatial structure of precipitation from four reanalysis—the fifth global atmospheric analysis dataset of the European Center for Medium‐Range Weather Forecasts (ECMWF) (ERA5), Interim ECMWF Reanalysis (ERA‐Interim), Japanese 55‐year Reanalysis project (JRA55) and Modern‐Era Retrospective analysis for Research and Applications, Version 2 (MERRA‐2), against the rain‐gauges over the PLB during 1980–2018. The results showed that the detection capacity of ERA5 and MERRA2 shifted around 2002, whereas that of ERA‐Interim and JRA55 did not substantially change. Before 2002, ERA‐Interim had the lowest biases for the magnitude of annual, monthly and different intensities of precipitation (except for heavy rain), while ERA5 and MERRA2 had strengths in capturing spatial correlation. Notably, ERA5 and MERRA2 had region‐wide wet and dry biases, respectively, before 2002, which can be attributed mainly to the estimation of flood season precipitation (from April to June) and heavy rain. In contrast, the fractional contribution of light–moderate rain was underestimated and overestimated by ERA5 and MERRA2, respectively. However, after 2002, because of the improved reproducibility of flood season precipitation and heavy rain magnitude, the systematic biases of ERA5 and MERRA2 significantly decreased, especially for ERA5, which outperformed other RPDs on all aspects of precipitation. The conclusions of this paper suggest that the variation of applicability over time should be seriously tested before the utilization of RPDs in hydro‐climate analyses. [ABSTRACT FROM AUTHOR]

The cold surge (CS) is an extreme climate event that frequently affects China. By using a 0.25° × 0.25° gridded observation dataset provided by the China Meteorological Administration (CN05.1), this paper investigates the spatial–temporal characteristics of various CSs in China during 1962–2018. According to the duration and coverage area (CA), CSs are classified into the short‐lived cold surges (SLCSs), the persistent cold surges (PCSs) and the extensive and persistent cold surges (EPCSs). They are also divided into the weak cold surges (WCSs), the strong cold surges (SCSs) and the super strong cold surges (SSCSs) based on the temperature drop (ΔT). The extreme cold surges (ECSs) are also defined and occur 38 times over 57 years. The total CSs and various CSs all display patterns of higher frequencies in the northeast and lower frequencies in the southwest. Northeast China and Inner Mongolia are frequently hit by various CSs, but the frequencies show overall downward trends in the regions during 1962–2018. The ΔT of the total CSs and ECSs both show significant decreasing trends in the whole of China from 1962 to 2018. From 1962 to 2018, the annual frequency and the ΔT of the total CSs both show decreasing trends. SLCSs and WCSs frequencies show overall increasing trends, but other kinds of CSs show overall decreasing trends. The annual frequencies of the total CSs and various CSs all have trend abrupt changes around 2000. The ΔT of ECSs shows a sharply increasing trend during 1962–2018. The total CSs and all kinds of CSs frequencies have a significant periodic oscillation. ECSs occur most in February. The total CSs and other kinds of CSs occur most in January. There are obvious differences between the multiyear average ECSs frequencies before 2000 and after 2000. [ABSTRACT FROM AUTHOR]

Moisture transport, associated with moisture sources and synoptic‐scale weather conditions, is a key dynamic process of precipitation events. Using the K‐means clustering method and the FLEXPART Lagrangian particle dispersion model, this paper investigates moisture contributions from different source regions to extreme precipitation in the first rainy season (hereafter FRS) over South China. In average, land regions contribute more to the FRS extreme precipitation over South China than the ocean regions. The main source regions are Southeast Asia (22.7%), South China (17.2%), the South China Sea (14.3%), and the Bay of Bengal (8.3%). Extreme precipitation events are classified into three types by the K‐means clustering based on 850 hPa geopotential height, which are all characterized by an anomalous low‐pressure system over South China with varying intensity and locations. The distribution of geopotential height anomaly for Type I (30.3%) is characterized the low trough extending from Japan to South China, while Type II (42.5%) and Type III (27.2%) are characterized by "west negative–east positive" and "north positive–south negative" patterns over East Asia with anomalous cyclone over South China, respectively. The much larger contribution of land sources than ocean regions are mainly concentrated in Type I and Type III, of which the contribution from each source region is similar. Ocean sources play a more important role in Type II and are mainly from the Indian Ocean (16.2%) associated with the onset of South China Sea summer monsoon. [ABSTRACT FROM AUTHOR]

The amount of winter precipitation over southeast China (SCWP) is significantly affected by the El Niño–Southern Oscillation (ENSO), with generally an enhanced SCWP response to El Niño events. In this paper, we find that this relationship shows remarkable nonstationarities on the multidecadal timescale. Running ENSO–SCWP correlation maintains statistically significant before the mid‐1960s and after the mid‐1990s but not during the interim period, which exhibits an approximate synchronized temporal evolution with the Atlantic Multidecadal Oscillation (AMO). A statistical method for interpreting nonstationary relationships is then applied to quantitatively verify and measure this possible AMO modulation. It is demonstrated that the nonstationary ENSO–SCWP correlation can be expressed to a large extent by the influence of the AMO–ENSO nonlinear effect (AMO*Niño3.4) on the SCWP. This influence intensifies the ENSO–SCWP relationship during the positive AMO phase but weakens it during the negative AMO phase. Therefore, the nonstationarity in the ENSO–SCWP relationship comes predominantly from an AMO modulation via this key nonlinear effect. We further suggest that a positive AMO favours a westward shift of ENSO sea surface temperature (SST) anomaly pattern in the tropical Pacific. Correspondingly, the anomalous western North Pacific (WNP) anticyclone also migrates westward, which gives rise to pronounced moisture transports and vertical motions over the adjacent southeast China, thereby resulting in a significant ENSO–SCWP relationship. In contrast, during the negative AMO phase, the eastward‐shifted ENSO SST and WNP anticyclonic anomalies produce significant atmospheric anomalies primarily to the east of the Chinese mainland, thus having less effect on the SCWP. [ABSTRACT FROM AUTHOR]

The hydrological projections provided by the outputs of Global Climate Models (GCMs) combining hydrological models include multi‐source uncertainties, which may challenge the formulation of relevant adaption and mitigation policies. In this paper, the overall uncertainty and the relative contribution of each uncertainty component were investigated for hydrological projections over 408 watersheds in China by using 3 shared socioeconomic pathway emission scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5), 21 GCMs, 8 bias correction methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. The results show that the total uncertainty (T) is mainly contributed by uncertainty related to global climate models (G), with the mean percentage ranging from 60.4 to 64.1%, followed by the interaction uncertainties among all components, with the mean percentage ranging from 22.0 to 26.4%. The uncertainty contribution of hydrological models (H) (6.1–9.4%) ranks third, followed by emission scenarios (S) (2.9–5.9%) and bias correction methods (B) (0.2–1.1%). The uncertainty contribution of the optimized hydrological model parameters (P) (0.2–0.3%) is almost negligible. In terms of spatial variability, the relative contribution of uncertainty related to global climate models (G) is the highest in the near future for northern China (67.5–70.6%) and in the far future for southern China (66.1–66.7%). However, it was found to be lower for the Tibetan Plateau and northwestern China (45.3–57.9%) in the near and far future. The relative contribution of hydrological model uncertainty is higher for southwestern and northwestern China and the Tibetan Plateau (7.2–19.5%) and lower for northern, eastern, and southern China (2.5–6.6%). This study highlights the importance of including multiple GCMs and hydrological models in hydrological impact studies to consider their overall uncertainty. The development of global climate models and hydrological models is still the best way to reduce the uncertainty of climate change impact studies. [ABSTRACT FROM AUTHOR]

In this paper, the connection between the November northeast Asian snow cover and the following winter precipitation over southern China is studied for the period of 1979–2014. The results indicate that, when there is more November snow cover over northeast Asia, the precipitation in southern China is depressed during the following January, and vice versa. Mechanism analysis suggests that the troposphere‐stratosphere interaction may play an important role in the delayed connection between the two. The more November snow cover over northeast Asia could enhance the vertical propagation of the wave activity, and it could also weaken the westerly winds over northern Asia in the troposphere by decreasing the meridional temperature gradient over the region. The persistent weakening of the tropospheric westerly winds is in favour of the upward propagation of the wave activity into the stratosphere in November and December, and the associated convergence anomalies of the E‐P flux in the stratosphere induce the weakened stratospheric polar vortex. Thereafter, the abnormal polar vortex propagates downward to the lower troposphere and produces a low pressure anomaly to the east of Lake Baikal during January. Such a low pressure anomaly tends to strengthen the southward invasion of the cold air to southern China, which is unfavourable for the precipitation there. This leading signal of the November snow cover over northeast Asia provides a potential source for the prediction of the January precipitation over southern China. [ABSTRACT FROM AUTHOR]

Along with the rapid urbanization and economic development, the Yangtze River Middle‐Reach urban agglomeration (YRMR) has suffered from increasingly serious environmental problems. Characterizing the long‐term dynamics of aerosol optical depth (AOD) in YRMR is of important significance for scientific control of aerosol pollutants over this region. Previously, numerous researches have analysed the temporal and spatial characteristics of AOD in different regions through statistical values of AOD time series or using trend analysis methods based on linear regression models. It is challenging to approximate nonlinear trends and identify change points of AOD time series. This paper proposed a new methodology for investigating the dynamics of AOD over the YRMR region on a monthly basis by using the Terra MODIS Collection 6.1 Level 2 aerosol products (MOD04_L2) during 2001–2017. A three‐dimensional spatio‐temporal interpolation method was used to generate gap‐filled monthly AOD maps, and then a generic Bayesian time series decomposition algorithm was applied to analyse the trends and change points of monthly AOD time series. The results showed that the areas with high AOD values were mainly located in the core cities and the connecting belts. Strong positive AOD anomalies had been observed during 2006–2011, whereas the positive anomalies were typically intermingled with negative anomalies and it indicated that the evolution patterns of AOD for different regions were different and influenced by fluctuations. Trend analysis of the monthly mean AOD values indicated that AOD in YRMR increased first, then fluctuated, and finally decreased. Although it was difficult to separate the increasing and fluctuating periods from each other due to the fluctuation, the decreasing trend after 2011 was significant, and a significant drop of AOD had been observed since 2014. Long‐term AOD variations were closely related to national policies, and the results could provide useful references for regional environmental assessment and decision‐making. [ABSTRACT FROM AUTHOR]

This paper analyses the different relationships between the two types of South Asian highs (SAH)—the southern mode of the SAH (SSAH) and the traditional SAH in its entirety (ESAH)—with respect to the variations in summer rainfall over East Asia. The ESAH is closely related to summer rainfall over northeastern China, whereas the SSAH is closely linked to summer rainfall over the Yangtze River valley, northern China, and Japan. Above‐normal rainfall tends to occur over northeastern China in strong ESAH years, whereas strong SSAH years tend to yield above‐normal rainfall over the Yangtze River valley and Japan together with below‐normal rainfall over northern China. The westerly jet and the western Pacific subtropical high (WPSH) shifts northward associated with a strengthened ESAH. The related anomalous southwesterly winds to the northwestern side of the WPSH transports abundant water vapour to northeastern China and leads to above‐normal rainfall there. In contrast, the westerly jet shifts southward and WPSH shifts southwestward to the South China Sea associated with a strengthened and southeastward‐shifted SSAH. Accordingly, a cyclonic anomaly appears over Northeast Asia and mid‐latitude western northern Pacific and an anticyclonic anomaly occurs over subtropical western North Pacific. The associated southwesterly wind anomalies over the Yangtze River valley and Japan contributes to above‐normal rainfall over these regions via transporting sufficient amount of water vapour. In addition, the anomalous northerly winds to the western flank of the cyclonic anomaly results in below‐normal rainfall over northern China via carrying drier air southward. Furthermore, the preceding winter ENSO events may play a more important role in the SSAH variation via modulating India Ocean SST. [ABSTRACT FROM AUTHOR]

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]

A good understanding of spatiotemporal characteristics of historical droughts is important for managing and tackling drought hazards. This paper aims to investigate spatiotemporal variations of drought in mainland China over 1961–2016 based on the standardized precipitation evapotranspiration index (SPEI) at the 1‐ and 12‐month timescales. The trend significance and magnitude of 12‐month SPEI were analysed using the modified Mann–Kendall (MMK) method and Sen's slope values. The empirical orthogonal function (EOF) and ensemble empirical mode decomposition (EEMD) were applied to analyse the spatial patterns and periods in 1‐month SPEI series. The results showed that the temporal fluctuations of SPEI were weaker as the timescale increased. The droughts in 1960s were the severest than in other decades. The sites with SPEI < −5 (denoting the extreme droughts) mainly located in the northwestern and southwestern China. An overall wetter trend was detected in most regions of China, except the belt from northeastern to middle and southwestern China. The 1‐month SPEI had shorter than 1.20‐year of mean periods for the seven sub‐regions and China, while had 0.22–2.95 years of mean periods at different sites. Residuals (decomposed from 1‐month SPEI) showed increasing trends in most of the sub‐regions except sub‐region 3. In northern and northwestern China, longer periods of SPEI were detected. The highest values of EOF1 and EOF2 illustrated the eastern and northeastern China were drought‐sensitive areas. From the correlations between 1‐month SPEI and the atmospheric circulations, the AO was the main physical factor that influenced drought occurrence in mainland China. The identified spatiotemporal characteristics of SPEI could offer references for drought prevention and management in China. [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]

The roles of moisture sources and transport in intraseasonal precipitation variabilities during the preflood season over South China are complex and different. In this paper, ERA‐Interim reanalysis data that were collected four times daily from 1979 to 2017 are used to analyse the variation and influence of moisture transport in intraseasonal precipitation during the preflood season over South China. Using the Hybrid Single‐Particle Lagrangian Integrated Trajectory model, fixed atmospheric moisture sources are defined: land area source (LD source), Indian Ocean source (IO source), Pacific Ocean source (PO source), South China Sea source (SCS source), and South China source (SC source). Sea source moisture transport accounts for more than 75% of the total precipitation during the preflood season, which is the most important moisture source in South China. For April, southeasterly moisture transport, which includes PO source and SCS source moisture transports, is the major moisture transport over South China. Then, the IO source and SCS source precipitation increased significantly during May. During June, the IO source precipitation accounted for 43.75% of the total precipitation, thus dominating precipitation over South China. In addition, there is a significant difference in the interdecadal variation in the preflood season precipitation before and after the Indian summer monsoon onset. Before the Indian summer monsoon onset, the land‐sea thermal contrast decreased, which led to a significant interdecadal reduction in the IO source, PO source, and SCS source moisture transports. After the Indian summer monsoon onset, the increase in IO source moisture transport is the primary cause of the interdecadal increase in precipitation over South China. [ABSTRACT FROM AUTHOR]

Seasonal prediction of extreme precipitation has long been a challenge especially for the East Asian Summer Monsoon region, where extreme rains are often disastrous for the human society and economy. This paper introduces a decision‐tree (DT) method for predicting extreme precipitation in the rainy season over South China in April–June (SC‐AMJ) and the North China Plain in July–August (NCP‐JA). A number of preceding climate indices are adopted as predictors. In both cases, the DT models involving ENSO and NAO indices exhibit the best performance with significant skills among those with other combinations of predictors and are superior to their linear counterpart, the binary logistic regression model. The physical mechanisms for the DT results are demonstrated by composite analyses of the same DT path samples. For SC‐AMJ, an extreme season can be determined mainly via two paths: the first follows a persistent negative NAO phase in February–March; the second goes with decaying El Niño. For NCP‐JA, an extreme season can also be traced via two paths: the first is featured by "non El Niño" and an extremely negative NAO phase in the preceding winter; the second follows a shift from El Niño in the preceding winter to La Niña in the early summer. Most of the mechanisms underlying the decision rules have been documented in previous studies, while some need further studies. The present results suggest that the decision‐tree approach takes advantage of discovering and incorporating various nonlinear relationships in the climate system, hence is of great potential for improving the prediction of seasonal extreme precipitation for given regions with increasing sample observations. [ABSTRACT FROM AUTHOR]

Drought occurs more frequently in the context of climate change and threatens water security worldwide. An appropriate fitting distribution is crucial for accurately identifying drought using drought indices. Here, seven two‐parameter distributions (Gamma, Gumbel, Logistic, Log–Logistic, Log‐normal, Normal, and Weibull) and four distributions (general logistic, generalized extreme value, Normal, and Pearson Type III) are applied to calculate the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), respectively, to choose the most appropriate fit for the 541 stations in China. The results show that, in most cases, Gamma and general logistic were the best distributions for SPI and SPEI, respectively. Nevertheless, we noted that Pearson Type III and generalized extreme value also fit the SPEI series well, indicating the importance of distribution fitting assessment for various regions. The annual and seasonal drought evolutions across China were clarified, with drought decreasing significantly in western Northwest China (annually and each season), northern North China (spring and winter), the Tibetan Plateau (spring), and the lower reaches of the Yangtze River (winter) and increasing mainly in South China (spring) and western South China (summer and autumn). The intensity, duration and severity of light, moderate, and severe drought were also detected; the results suggest that drought in China is mainly concentrated around the changes in light and moderate drought. Additionally, we assessed the sensitivity of drought evolution to various meteorological variables. The most sensitive variable in South China, North China and the Tibetan Plateau is precipitation, followed by wind speed, temperature, relative humidity, and sunshine duration; in Northwest China, the order is wind speed, precipitation, temperature, relative humidity, and sunshine duration. Furthermore, the correlation results also indicate that the drought evolution is affected by the multivariate ENSO index, while the influencing characteristics are different in various seasons and regions. The results in this paper contribute to drought mitigation and the effective utilization of water resources in China. [ABSTRACT FROM AUTHOR]

In this paper, we evaluate a 48‐member multiphysics ensemble using the Weather Research and Forecasting (WRF) model for the JJA extreme precipitation and temperature in 2003 over the CORDEX‐EA‐II domain. The simulated precipitation and temperature are reasonable in the subregions controlled by a large‐scale circulation, yet the biases for both precipitation and temperature are evident over the subregions where the effects of mesoscale processes are important. The performance of various combinations of WRF physical schemes for simulating the JJA precipitation is dependent on the region. Meanwhile, the cumulus and microphysical schemes have substantial influences on the simulation of precipitation, and the land surface models and cumulus schemes play crucial roles in the surface temperature. Our analysis shows that the combination of Noah for the land surface process, Lin for the microphysics, G3D for the cumulus parameterization, and CAM for the radiation scheme can provide the most reliable reproduction of both precipitation and temperature extremes over China. Ensemble analysis shows that the simulated climate extremes are usually accompanied by a large ensemble spread, implying sensitivities to the model physical processes in some subregions. The simulated wind fields at low‐to‐middle atmospheric levels display responses to the options of the land surface models and cumulus schemes. The essential impact of the land–atmospheric interaction on simulating the extremes can be largely attributed to the active convective processes. WRF has difficulties in reproducing the observed temporal evolution of the rainfall process, which consists of continuous large rainfall episodes in the observation data set during the simulation period. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents an evaluation study of the Regional Climate Model version 4.3 ( RegCM4.3) over the Loess Plateau in northern China, which is a semi-arid region characterized by complex topography. During recent years, a series of reforestation programmes have been implemented across the region that might influence the local climate in complex ways. To better understand the local climate, the RegCM4.3 was applied to simulate the present-day conditions over the Loess Plateau. The simulation was carried out from 1990 to 2009 at the 50-km horizontal resolution, with lateral boundary conditions taken from the ECMWF-Interim reanalysis. A series of climate variables and processes were evaluated during the winter and summer seasons, such as 2-m air temperature, precipitation, wind circulation, surface energy balance, full moisture budget, and cloud radiative forcing (CRF). The possible origins of the simulation bias and the physical linkages with other model processes were examined. In general, RegCM4.3 is able to reproduce both the spatial and temporal features of the regional climate over the Loess Plateau. However, there are still biases in some meteorological variables including precipitation and 2-m air temperature. In particular, the model tends to produce cold biases during winter and underestimate precipitation during summer. Further analyses indicates that the cold biases in winter may have resulted from the deficiency of the downward longwave radiation fluxes, excessive ground heat fluxes, and negative temperature advection by the seasonal mean circulation. These processes are primarily triggered by deficiencies in CRF and excessive northwesterlies over the plateau. The underestimated precipitation during summer is associated with a weak southerly monsoon in the model. A full moisture budget analysis reveals that the dry bias in this region can be mainly attributed to model deficiency in moisture advection and convergence, and to a lesser extent to that in surface evaporation. [ABSTRACT FROM AUTHOR]

ABSTRACT The biases in the Global Circulation Models ( GCMs) are crucial for understanding future climate changes. Currently, most bias correction methodologies suffer from the assumption that model bias is stationary. This paper provides a non-stationary bias correction model, termed residual-based bagging tree ( RBT) model, to reduce simulation biases and to quantify the contributions of single models. Specifically, the proposed model estimates the residuals between individual models and observations, and takes the differences between observations and the ensemble mean into consideration during the model training process. A case study is conducted for 10 major river basins in Mainland China during different seasons. Results show that the proposed model is capable of providing accurate and stable predictions while including the non-stationarities into the modelling framework. Significant reductions in both bias and root mean squared error are achieved with the proposed RBT model, especially for the central and western parts of China. The proposed RBT model has consistently better performance in reducing biases when compared with the raw ensemble mean, the ensemble mean with simple additive bias correction, and the single best model for different seasons. Furthermore, the contribution of each single GCM in reducing the overall bias is quantified. The single model importance varies between 3.1% and 7.2%. For different future scenarios ( RCP 2.6, RCP 4.5, and RCP 8.5), the results from RBT model suggest temperature increases of 1.44, 2.59, and 4.71 °C by the end of the century, respectively, when compared with the average temperature during 1970-1999. [ABSTRACT FROM AUTHOR]

ABSTRACT Climate is a primary driver for extreme rainfall and flood events. In this paper, the temporally changing flood risk associated with the annual maximum 30-day rainfall (30-day AMR) and the annual maximum daily inflow ( AMDI) related to the Xidayang Reservoir catchment is analysed from a climatic context. This is the largest catchment in Daqing River Basin in North China and is highly prone to floods related to the East Asian summer monsoon. Two climate factors, the average May-June-July sea surface temperature anomalies in areas of the northern Indian Ocean and western Pacific Ocean, are identified. They show a high negative correlation with the AMDI and 30-day AMR. Bayesian nonstationary models are then developed for the AMDI and 30-day AMR using the climate predictors identified as covariates. We compared three types of models of AMDI and 30-day AMR: (1) time-invariant, (2) linear temporal trend and (3) climate informed, and found that the climate-informed models exhibit the best performance according to Deviance Information Criterion (DIC) and 90th percentile Bayesian coverage rate for both AMDI and 30-day AMR. A significant decreasing trend is identified in the AMDI and the 30-day AMR, which is found to be associated with the climate predictors. Leave one out cross validation (LOOCV) is used to demonstrate that these models have decent skill in predicting year-to-year variability in flood risk. This can help to provide flood/rainfall dynamic management measures for reservoirs in Daqing River Basin using information from before the beginning of monsoon season, thus facilitating adaptation to a changing climate. [ABSTRACT FROM AUTHOR]

The effects of aridity on ecosystems and water cycles are pronounced and have received considerable attention. However, aridity changes due to future warming and its regional variability over China remain uncertain. This paper aims to identify the spatiotemporal variations in aridity and its key influencing factors over China in the mid-21st century based on five general circulationmodels (GCMs) and four representative concentration pathway (RCP) scenarios. An aridity index (AI), defined as the ratio of reference evapotranspiration (ETo) to precipitation (P), was calculated. We show that the GCMensemble means are able to reproduce the variation of aridity during the baseline period. Generally, ETo anomalies are consistently positive. Other than for the RCP2.6 low-emission scenario, precipitation and aridity are both projected to increase. There are pronounced regional differences in aridity changes; i.e. wetter across most of western China and drier across most of eastern China in the mid-21st century. Negative AI anomalies in western China can be attributed mainly to the projected increase in precipitation. In eastern China, the AI was higher despite positive precipitation anomalies, due mainly to the greater effect of climate change on increasing atmospheric moisture demand. This suggests that evapotranspiration demand should be incorporated into aridity changes under future warming. [ABSTRACT FROM AUTHOR]

ABSTRACT High-resolution tree-ring records covering past centuries in the western Sichuan Plateau of China are scarce, yet essential for developing a complete understanding of the regional patterns of climate change for the Tibetan plateau. This paper presents a temperature reconstruction for the Wolong Natural Reserve of the western Sichuan Plateau based on a tree-ring width chronology of a cypress species ( Sabina saltuaria) endemic to the Tibetan plateau. The annual (prior October to current September) temperature reconstruction, which spans from 1840 to 2011, was developed using a linear regression model. The reconstruction explains 54.1% of the actual temperature variance during the calibration period from 1956 to 2010. The noteworthy warming periods of the reconstruction occurred between 1864-1882 and 1927-1960, while the most pronounced cold episodes were found between 1886-1926, 1788-1822, and 1961-1996. Spectral analysis revealed the existence of multi-decadal, decadal, and annual cycles that might correspond to the related cycles of Atlantic Multidecadal Oscillation, solar activity, and ENSO-type variability, respectively. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, we present the response of actual evaporation to global warming in China based on the National Centers for Environmental Prediction-National Center for Atmospheric Research ( NCEP/ NCAR) reanalysis 1, the NCEP-Department of Energy ( NCEP/ DOE) reanalysis 2, 40-year ECMWF Re-Analysis ( ERA-40), ECMWF-Interim Re-Analysis ( ERA-Interim), Modern-Era Retrospective Analysis for Research and Applications ( MERRA), and Climate Forecast System Reanalysis ( CFSR). Rotated empirical orthogonal function ( REOF) analysis was applied to reveal the coherent pattern of the change in reanalysis evaporation ( RE). According to the first four REOF modes in NCEP/ NCAR, we subdivided China into six climatic regions, and found that RE increased significantly in central and southeast of China from 1979 to 2012, especially during the sub-period of 1985-2004. Various estimates of linear trend are statistically consistent, and most of them are significant at the 95% confidence level. Conversely, RE decreased in northern and northeast although the magnitude of decreasing trends is inconsistently among the reanalysis. Moreover, there is a general tendency of RE to increase in northwest, but mostly RE trends are not significant. In the dry northwest and northern regions, rainfall is an important factor affecting the changing of RE, while RE is less sensitive to the change of temperature. However, RE rates are dominated by temperature in the humid central and southeast regions. These results support the view that the sensitivity of RE to global warming largely depends on the environment. In view of data availability, despite the general similarity, differences exist among the various reanalysis, especially in NCEP/ NCAR and CFSR. In summary, it can be concluded that the quality of RE values is higher in central and southeast, while even greater uncertainties lie in the estimates of RE in northwest China and Qinghai-Tibet Plateau. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper focuses on testing appropriate methods to produce gridded rainfall surfaces based on the daily rainfall observed at 43 meteorological stations in the Sichuan Province during rainy seasons of 2008-2013. Rainfall is extremely variable in both spatial and temporal distribution. Therefore, three methods, namely Ordinary Kriging ( OK), Inverse Distance Weighting ( IDW) and ordinary CoKriging ( CK), have been selected and examined. First, we choose a suitable model by adjusting relevant parameters including the power exponent, the Neighbours to Include and the Include at Least. Then, cross-validation is used to verify the prediction performance of the three methods. The results for spatial interpolation of daily rainfall show that: (1) both OK and CK methods recorded smaller mean absolute error and the root-mean-square error than IDW method. (2) A preferable combination of assigning 2 to power exponent, 15 to Neighbours to Include and 5 to Include at Least is identified for IDW method. As for the best combination of OK and CK methods, Neighbours to Include is assigned with 15 while Include at Least almost has no effect on their performance. (3) CK is the optimal method for spatial interpolation of the daily rainfall in the rainy season of Sichuan. [ABSTRACT FROM AUTHOR]