Paleoclimate Modelling at the Institute of Atmospheric Physics, Chinese Academy of Sciences

Paleoclimate modelling is one of the core topics in the Past Global Changes project under the International Geosphere-Biosphere Programme and has received much attention worldwide in recent decades. Here we summarize the research on the Paleoclimate modeling, including the Holocene, Last Glacial Maximum, and pre-Quaternary climate intervals or events performed at the Institute of Atmospheric Physics under the Chinese Academy of Sciences （IAP/CAS） for over one decade. As an attempt to review these academic activities, we emphasize that vegetation and ocean feedbacks can amplify East Asian climate response to the Earth＇s orbital parameters and atmospheric CO2 concentration at the mid-Holocene. At the Last Glacial Maximum, additional cooling in interior China is caused by the feedback effects of East Asian vegetation and the ice sheet over the Tibetan Plateau, and the regional climate model RegCM2 generally reduces data-model discrepancies in East Asia. The simulated mid-Pliocene climate is characterized by warmer and drier conditions as well as significantly weakened summer and winter monsoon systems in interior China. On a tectonic timescale, both the Tibetan Plateau uplift and the Paratethys Sea retreat play important roles in the formation of East Asian monsoon-dominant environmental pattern during the Cenozoic.

Time-lagged Effects of the Spring Atmospheric Heat Source over the Tibetan Plateau on Summer Precipitation in Northeast China during 1961–2020:Role of Soil Moisture

The spring atmospheric heat source(AHS)over the Tibetan Plateau(TP)has been suggested to affect the Asian summer monsoon and summer precipitation over South China.However,its influence on the summer precipitation in Northeast China(NEC)remains unknown.The connection between spring TP AHS and subsequent summer precipitation over NEC from 1961 to 2020 is analyzed in this study.Results illustrate that stronger spring TP AHS can enhance subsequent summer NEC precipitation,and higher soil moisture in the Yellow River Valley-North China region(YRVNC)acts as a bridge.During spring,the strong TP AHS could strengthen the transportation of water vapor to East China and lead to excessive rainfall in the YRVNC.Thus,soil moisture increases,which regulates local thermal conditions by decreasing local surface skin temperature and sensible heat.Owing to the memory of soil moisture,the lower spring sensible heat over the YRVNC can last until mid-summer,decrease the land–sea thermal contrast,and weaken the southerly winds over the East Asia–western Pacific region and convective activities over the South China Sea and tropical western Pacific.This modulates the East Asia–Pacific teleconnection pattern,which leads to a cyclonic anomaly and excessive summer precipitation over NEC.

Relative Impacts of Sea Ice Loss and Atmospheric Internal Variability on the Winter Arctic to East Asian Surface Air Temperature Based on Large-Ensemble Simulations with NorESM2

To quantify the relative contributions of Arctic sea ice and unforced atmospheric internal variability to the “warm Arctic, cold East Asia”(WACE) teleconnection, this study analyses three sets of large-ensemble simulations carried out by the Norwegian Earth System Model with a coupled atmosphere–land surface model, forced by seasonal sea ice conditions from preindustrial, present-day, and future periods. Each ensemble member within the same set uses the same forcing but with small perturbations to the atmospheric initial state. Hence, the difference between the present-day(or future) ensemble mean and the preindustrial ensemble mean provides the ice-loss-induced response, while the difference of the individual members within the present-day(or future) set is the effect of atmospheric internal variability. Results indicate that both present-day and future sea ice loss can force a negative phase of the Arctic Oscillation with a WACE pattern in winter. The magnitude of ice-induced Arctic warming is over four(ten) times larger than the ice-induced East Asian cooling in the present-day(future) experiment;the latter having a magnitude that is about 30% of the observed cooling. Sea ice loss contributes about 60%(80%) to the Arctic winter warming in the present-day(future) experiment. Atmospheric internal variability can also induce a WACE pattern with comparable magnitudes between the Arctic and East Asia. Ice-lossinduced East Asian cooling can easily be masked by atmospheric internal variability effects because random atmospheric internal variability may induce a larger magnitude warming. The observed WACE pattern occurs as a result of both Arctic sea ice loss and atmospheric internal variability, with the former dominating Arctic warming and the latter dominating East Asian cooling.

The Persistence and Zonal Scale of Atmospheric Dipolar Modes

This study investigates the relationship between the persistence and the zonal scale of atmospheric dipolar modes(DMs). Results from the daily data of ERA5 and the long-term output of an idealized atmospheric model show that the atmospheric DMs with a broader(narrower) zonal scale dipolar structure possess a longer(shorter) persistence. A detailed vorticity budget analysis indicates that the persistence of a hemispheric-scale DM(1/1 DM) and a regional or sectoral DM(1/8 DM) in the model both largely rely on the persistence of the nonlinear eddy forcing. Linear terms can indirectly reduce the persistence of the anomalous nonlinear eddy forcing in a 1/8 DM by modifying the baroclinicity via the arousal of anomalous vertical motions. Therefore, the atmospheric DMs with a broader(narrower) zonal scale possess a longer(shorter) persistence because the effects of the linear terms are less(more) pronounced when the atmospheric DMs have better(worse) zonal symmetry. Further analyses show that the positive eddy feedback effect is weak or even absent in a 1/8DM and the high-frequency eddy forcing acts more like a concomitant phenomenon rather than a leading driving factor for a 1/8 DM. Thus, the hemispheric-scale DM and the regional or sectoral DMs are different, not only in their persistence but also in their dynamics.

Contrast in Secondary Organic Aerosols between the Present Day and the Preindustrial Period:The Importance of Nontraditional Sources and the Changed Atmospheric Oxidation Capability

Quantifying differences in secondary organic aerosols(SOAs)between the preindustrial period and the present day is crucial to assess climate forcing and environmental effects resulting from anthropogenic activities.The lack of vegetation information for the preindustrial period and the uncertainties in describing SOA formation are two leading factors preventing simulation of SOA.This study calculated the online emissions of biogenic volatile organic compounds(VOCs)in the Aerosol and Atmospheric Chemistry Model of the Institute of Atmospheric Physics(IAP-AACM)by coupling the Model of Emissions of Gases and Aerosols from Nature(MEGAN),where the input vegetation parameters were simulated by the IAP Dynamic Global Vegetation Model(IAP-DGVM).The volatility basis set(VBS)approach was adopted to simulate SOA formation from the nontraditional pathways,i.e.,the oxidation of intermediate VOCs and aging of primary organic aerosol.Although biogenic SOAs(BSOAs)were dominant in SOAs globally in the preindustrial period,the contribution of nontraditional anthropogenic SOAs(ASOAs)to the total SOAs was up to 35.7%.In the present day,the contribution of ASOAs was 2.8 times larger than that in the preindustrial period.The contribution of nontraditional sources of SOAs to SOA was as high as 53.1%.The influence of increased anthropogenic emissions in the present day on BSOA concentrations was greater than that of increased biogenic emission changes.The response of BSOA concentrations to anthropogenic emission changes in the present day was more sensitive than that in the preindustrial period.The nontraditional sources and the atmospheric oxidation capability greatly affect the global SOA change.

The First Global Map of Atmospheric Ammonia(NH_(3)) as Observed by the HIRAS/FY-3D Satellite

Atmospheric ammonia(NH_(3)) is a chemically active trace gas that plays an important role in the atmospheric environment and climate change. Satellite remote sensing is a powerful technique to monitor NH_(3) concentration based on the absorption lines of NH_(3) in the thermal infrared region. In this study, we establish a retrieval algorithm to derive the NH_(3)column from the Hyperspectral Infrared Atmospheric Sounder(HIRAS) onboard the Chinese Feng Yun(FY)-3D satellite and present the first atmospheric NH_(3) column global map observed by the HIRAS instrument. The HIRAS observations can well capture NH_(3) hotspots around the world, e.g., India, West Africa, and East China, where large NH_(3) emissions exist. The HIRAS NH_(3) columns are also compared to the space-based Infrared Atmospheric Sounding Interferometer(IASI)measurements, and we find that the two instruments observe a consistent NH_(3) global distribution, with correlation coefficient(R) values of 0.28–0.73. Finally, some remaining issues about the HIRAS NH_(3) retrieval are discussed.

A teleconnection pattern of upper-tropospheric circulation anomalies over the Eurasian continent associated with the interannual variability of atmospheric convection over the tropical western North Pacific in July

热带西北太平洋大气对流的年际变化对东亚夏季气候有着显著影响,而且受到前冬ENSO的影响.然而,还有相当多的西北太平洋对流年际变化不能由前冬ENSO解释.本文表明,7月西北太平洋对流受到欧亚大陆对流层上层遥相关型的显著影响,且这一影响与前冬ENSO的影响程度相当.该遥相关型表现为位于欧洲的气旋式环流异常,中亚的反气旋式环流异常和东亚带状延伸的气旋式环流异常,并表现为7月对流层上层环流的第一主模态.基于本文的结果并结合前人的研究,作者认为这一遥相关型能通过亚洲急流出口区附近的罗斯贝波破碎引起西北太平洋对流异常.

Westward extension of summer atmospheric circulation over the North Pacific after the 1990s

本文研究了20世纪90年代前后夏季北太平洋大气环流(NPcirculation)向西扩展,并探讨了其可能的原因.结果表明,在1961-1983年期间NP中心主要位于东北太平洋,而在1994-2016年期间NP中心发生西移,至北太平洋中部进一步分析指出,90年代以后NP活动中心的西移和春季中纬度北太平洋海温(SST_NP)与NP关系的加强有关.春季SST_NP异常通过引起后期夏季北太平洋中部地表热通量(即感热通量和潜热通量)和垂直运动异常,有利于NP活动中心向西移动,此外,20世纪90年代以后,北太平洋中部海平面气压的年际变率增加,这可能是NP向西扩展的另一原因。

Unprecedented Heatwave in Western North America during Late June of 2021: Roles of Atmospheric Circulation and Global Warming

An extraordinary and unprecedented heatwave swept across western North America(i.e.,the Pacific Northwest)in late June of 2021,resulting in hundreds of deaths,a massive die-off of sea creatures off the coast,and horrific wildfires.Here,we use observational data to find the atmospheric circulation variabilities of the North Pacific and Arctic-Pacific-Canada patterns that co-occurred with the development and mature phases of the heatwave,as well as the North America pattern,which coincided with the decaying and eastward movement of the heatwave.Climate models from the Coupled Model Intercomparison Project(Phase 6)are not designed to simulate a particular heatwave event like this one.Still,models show that greenhouse gases are the main reason for the long-term increase of average daily maximum temperature in western North America in the past and future.

A Perspective on the Evolution of Atmospheric Blocking Theories:From Eddy-Mean flow Interaction to Nonlinear Multiscale Interaction

In this paper,we first review the research advancements in blocking dynamics and highlight the merits and drawbacks of the previous theories of atmospheric blocking.Then,the dynamical mechanisms of atmospheric blocking are presented based on a nonlinear multi-scale interaction(NMI)model.Previous studies suggested that the eddy deformation(e.g.,eddy straining,wave breaking,and eddy merging)might lead to the formation and maintenance of atmospheric blocking.However,the results were speculative and problematic because the previous studies,based on the time-mean eddy-mean flow interaction model,cannot identify the causal relationship between the evolution of atmospheric blocking and the eddy deformation.Based on the NMI model,we indicate that the onset,growth,maintenance,and decay of atmospheric blocking is mainly produced by the spatiotemporal evolution of pre-existing upstream synoptic-scale eddies,whereas the eddy deformation is a concomitant phenomenon of the blocking formation.The lifetime of blocking is mainly determined by the meridional background potential vorticity gradient(PVy)because a small PVyfavors weak energy dispersion and strong nonlinearity to sustain the blocking.But the zonal movement of atmospheric blocking is associated with the background westerly wind,PVy,and the blocking amplitude.Using this NMI model,a bridge from the climate change to sub-seasonal atmospheric blocking and weather extremes might be established via examining the effect of climate change on PVy.Thus,it is expected that using the NMI model to explore the dynamics of atmospheric blocking and its change is a new direction in the future.

A Review of Atmospheric Electricity Research in China from 2019 to 2022

Atmospheric electricity is composed of a series of electric phenomena in the atmosphere.Significant advances in atmospheric electricity research conducted in China have been achieved in recent years.In this paper,the research progress on atmospheric electricity achieved in China during 2019-22 is reviewed focusing on the following aspects:(1)lightning detection and location techniques,(2)thunderstorm electricity,(3)lightning forecasting methods and techniques,(4)physical processes of lightning discharge,(5)high energy emissions and effects of thunderstorms on the upper atmosphere,and(6)the effect of aerosol on lightning.

Bias Analysis in the Simulation of the Western North Pacific Tropical Cyclone Characteristics by Two High-Resolution Global Atmospheric Models

This study compares the atmosphere-only HighResMIP simulations from FGOALS-f3-H(FGOALS)and MRIAGCM3-2-S(MRI)with respect to tropical cyclone(TC)characteristics over the Western North Pacific(WNP)for the July-October months of 1985-2014.The focus is on investigating the role of the tropical easterly jet over the Western Pacific(WP_TEJ)in modulating the simulation biases in terms of their climatological distribution and interannual variability of WNP TC genesis frequency(TCGF)based on the analysis of the genesis potential index(GPI).Results show that the two models reasonably capture the main TC genesis location,the maximum center of frequency,and track density;however,their biases mainly lie in simulating the intense TCs and TCGF distributions.The MRI better simulates the windpressure relationship(WPR)but overestimates the proportion of super typhoons(SSTYs).At the same time,FGOALS underestimates the WPR and the proportion of SSTYs but better simulates the total WNP TC precipitation.In particular,FGOALS overestimates the TCGF in the northeastern WNP,which is strongly tied to an overestimated WP_TEJ and the enhanced vertical circulation to the north of its entrance region.In contrast,the MRI simulates a weaker WP_TEJ and vertical circulation,leading to a negative TCGF bias in most of the WNP.Both models exhibit comparable capability in simulating the interannual variability of WP_TEJ intensity,but the composite difference of large-scale atmospheric factors between strong and weak WP_TEJ years is overestimated,resulting in larger interannual anomalies of WNP TCGF,especially for FGOALS.Therefore,accurate simulations of the WP_TEJ and the associated oceanic and atmospheric factors are crucial to further improving WNP TC simulations for both models.

El Niño and the AMO Sparked the Astonishingly Large Margin of Warming in the Global Mean Surface Temperature in 2023

In 2023,the majority of the Earth witnessed its warmest boreal summer and autumn since 1850.Whether 2023 will indeed turn out to be the warmest year on record and what caused the astonishingly large margin of warming has become one of the hottest topics in the scientific community and is closely connected to the future development of human society.We analyzed the monthly varying global mean surface temperature(GMST)in 2023 and found that the globe,the land,and the oceans in 2023 all exhibit extraordinary warming,which is distinct from any previous year in recorded history.Based on the GMST statistical ensemble prediction model developed at the Institute of Atmospheric Physics,the GMST in 2023 is predicted to be 1.41℃±0.07℃,which will certainly surpass that in 2016 as the warmest year since 1850,and is approaching the 1.5℃ global warming threshold.Compared to 2022,the GMST in 2023 will increase by 0.24℃,with 88%of the increment contributed by the annual variability as mostly affected by El Niño.Moreover,the multidecadal variability related to the Atlantic Multidecadal Oscillation(AMO)in 2023 also provided an important warming background for sparking the GMST rise.As a result,the GMST in 2023 is projected to be 1.15℃±0.07℃,with only a 0.02℃ increment,if the effects of natural variability—including El Niño and the AMO—are eliminated and only the global warming trend is considered.

Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO

The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key target.Previous studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO indices.The present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 models.Compared with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern Pacific.The CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.

The Tibetan Plateau bridge:Influence of remote teleconnections from extratropical and tropical forcings on climate anomalies

本文回顾了青藏高原桥梁作用方面的最新研究进展,涉及北大西洋气候异常对春,夏亚洲季风和厄尔尼诺-南方涛动(ENSO)事件的遥相关影响,热带海洋异常和中国东部极端气候异常之间的联系以及华南春雨的季节内变化等.介绍了年际时间尺度上,冬-春季北大西洋海表温度强迫如何影响南亚季风的季节性转变以及随后ENSO事件的触发.5月份青藏高原上空显著的负感热斜压结构,为北大西洋影响亚洲季风和ENSO提供了桥梁效应,夏季北大西洋涛动与华东夏季降水变化显著相关,高原潜热在这一关系中起着桥梁作用.另一方面,这种高原桥梁效应也存在于从热带海洋异常到东亚夏季极端降水事件的连接中,以及从中纬度波列到华南春雨准双周振荡的联系中.

Convection-Permitting Simulations of Current and Future Climates over the Tibetan Plateau

The Tibetan Plateau(TP)region,also known as the“Asian water tower”,provides a vital water resource for downstream regions.Previous studies of water cycle changes over the TP have been conducted with climate models of coarse resolution in which deep convection must be parameterized.In this study,we present results from a first set of highresolution climate change simulations that permit convection at approximately 3.3-km grid spacing,with a focus on the TP,using the Icosahedral Nonhydrostatic Weather and Climate Model(ICON).Two 12-year simulations were performed,consisting of a retrospective simulation(2008–20)with initial and boundary conditions from ERA5 reanalysis and a pseudoglobal warming projection driven by modified reanalysis-derived initial and boundary conditions by adding the monthly CMIP6 ensemble-mean climate change under the SSP5-8.5 scenario.The retrospective simulation shows overall good performance in capturing the seasonal precipitation and surface air temperature.Over the central and eastern TP,the average biases in precipitation(temperature)are less than−0.34 mm d−1(−1.1℃)throughout the year.The simulated biases over the TP are height-dependent.Cold(wet)biases are found in summer(winter)above 5500 m.The future climate simulation suggests that the TP will be wetter and warmer under the SSP5-8.5 scenario.The general features of projected changes in ICON are comparable to the CMIP6 ensemble projection,but the added value from kilometer-scale modeling is evident in both precipitation and temperature projections over complex topographic regions.These ICON-downscaled climate change simulations provide a high-resolution dataset to the community for the study of regional climate changes and impacts over the TP.

Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

Arctic Sea Ice Variations in the First Half of the 20th Century:A New Reconstruction Based on Hydrometeorological Data

The shrinking Arctic sea-ice area(SIA) in recent decades is a striking manifestation of the ongoing climate change.Variations of the Arctic sea ice have been continuously observed by satellites since 1979, relatively well monitored since the 1950s, but are highly uncertain in the earlier period due to a lack of observations. Several reconstructions of the historical gridded sea-ice concentration(SIC) data were recently presented based on synthesized regional sea-ice observations or by applying a hybrid model–empirical approach. Here, we present an SIC reconstruction for the period1901–2019 based on established co-variability between SIC and surface air temperature, sea surface temperature, and sea level pressure patterns. The reconstructed sea-ice data for March and September are compared to the frequently used Had ISST1.1 and SIBT1850 datasets. Our reconstruction shows a large decrease in SIA from the 1920 to 1940 concurrent with the Early 20th Century Warming event in the Arctic. Such a negative SIA anomaly is absent in Had ISST1.1 data. The amplitude of the SIA anomaly reaches about 0.8 mln km^(2) in March and 1.5 mln km^(2) in September. The anomaly is about three times stronger than that in the SIBT1850 dataset. The larger decrease in SIA in September is largely due to the stronger SIC reduction in the western sector of the Arctic Ocean in the 70°–80°N latitudinal zone. Our reconstruction provides gridded monthly data that can be used as boundary conditions for atmospheric reanalyses and model experiments to study the Arctic climate for the first half of the 20th century.

The Role of Underlying Boundary Forcing in Shaping the Recent Decadal Change of Persistent Anomalous Activity over the Ural Mountains

Observational analyses demonstrate that the Ural persistent positive height anomaly event(PAE) experienced a decadal increase around the year 2000, exhibiting a southward displacement afterward. These decadal variations are related to a large-scale circulation shift over the Eurasian Continent. The effects of underlying sea ice and sea surface temperature(SST) anomalies on the Ural PAE and the related atmospheric circulation were explored by Atmospheric Model Intercomparison Project(AMIP) experiments from the Coupled Model Intercomparison Project Phase 6 and by sensitivity experiments using the Atmospheric General Circulation Model(AGCM). The AMIP experiment results suggest that the underlying sea ice and SST anomalies play important roles. The individual contributions of sea ice loss in the Barents-Kara Seas and the SST anomalies linked to the phase transition of the Pacific Decadal Oscillation(PDO) and Atlantic Multidecadal Oscillation(AMO) are further investigated by AGCM sensitivity experiments isolating the respective forcings.The sea ice decline in Barents-Kara Seas triggers an atmospheric wave train over the Eurasian mid-to-high latitudes with positive anomalies over the Urals, favoring the occurrence of Ural PAEs. The shift in the PDO to its negative phase triggers a wave train propagating downstream from the North Pacific. One positive anomaly lobe of the wave train is located over the Ural Mountains and increases the PAE there. The negative-to-positive transition of the AMO phase since the late-1990s causes positive 500-h Pa height anomalies south of the Ural Mountains, which promote a southward shift of Ural PAE.

Assessing the Performance of CMIP6 Models in Simulating Droughts across Global Drylands

Both the attribution of historical change and future projections of droughts rely heavily on climate modeling. However,reasonable drought simulations have remained a challenge, and the related performances of the current state-of-the-art Coupled Model Intercomparison Project phase 6(CMIP6) models remain unknown. Here, both the strengths and weaknesses of CMIP6 models in simulating droughts and corresponding hydrothermal conditions in drylands are assessed.While the general patterns of simulated meteorological elements in drylands resemble the observations, the annual precipitation is overestimated by ~33%(with a model spread of 2.3%–77.2%), along with an underestimation of potential evapotranspiration(PET) by ~32%(17.5%–47.2%). The water deficit condition, measured by the difference between precipitation and PET, is 50%(29.1%–71.7%) weaker than observations. The CMIP6 models show weaknesses in capturing the climate mean drought characteristics in drylands, particularly with the occurrence and duration largely underestimated in the hyperarid Afro-Asian areas. Nonetheless, the drought-associated meteorological anomalies, including reduced precipitation, warmer temperatures, higher evaporative demand, and increased water deficit conditions, are reasonably reproduced. The simulated magnitude of precipitation(water deficit) associated with dryland droughts is overestimated by 28%(24%) compared to observations. The observed increasing trends in drought fractional area,occurrence, and corresponding meteorological anomalies during 1980–2014 are reasonably reproduced. Still, the increase in drought characteristics, associated precipitation and water deficit are obviously underestimated after the late 1990s,especially for mild and moderate droughts, indicative of a weaker response of dryland drought changes to global warming in CMIP6 models. Our results suggest that it is imperative to employ bias correction approaches in drought-related studies over drylands by using CMIP6 outputs.