Climate-change effects on extreme precipitation in central Europe: uncertainties of scenarios based on regional climate models

Observations as well as most climate model simulations are generally in accord with the hypothesis that the hydrologic cycle should intensify and become highly volatile with the greenhouse-gas-induced climate change, although uncertainties of these projections as well as the spatial and seasonal variability of the changes are much larger than for temperature extremes. In this study, we examine scenarios of changes in extreme precipitation events in 24 future climate runs of ten regional climate models, focusing on a specific area of the Czech Republic (central Europe) where complex orography and an interaction of other factors governing the occurrence of heavy precipitation events result in patterns that cannot be captured by global models. The peaks-over-threshold analysis with increasing threshold censoring is applied to estimate multi-year return levels of daily rainfall amounts. Uncertainties in scenarios of changes for the late 21st century related to the inter-model and within-ensemble variability and the use of the SRES-A2 and SRES-B2 greenhouse gas emission scenarios are evaluated. The results show that heavy precipitation events are likely to increase in severity in winter and (with less agreement among models) also in summer. The inter-model and intra-model variability and related uncertainties in the pattern and magnitude of the change is large, but the scenarios tend to agree with precipitation trends recently observed in the area, which may strengthen their credibility. In most scenario runs, the projected change in extreme precipitation in summer is of the opposite sign than a change in mean seasonal totals, the latter pointing towards generally drier conditions in summer. A combination of enhanced heavy precipitation amounts and reduced water infiltration capabilities of a dry soil may severely increase peak river discharges and flood-related risks in this region.     

Quantifying Changes in Extreme Weather Events in Response to Warmer Global Temperature

Global warming is expected to affect both the frequency and severity of extreme weather events, though projections of the response of these events to climate warming remain highly uncertain. The range of changes reported in the climate modelling literature is very large, sometimes leading to contradictory results for a given extreme weather event. Much of this uncertainty stems from the incomplete understanding of the physics of extreme weather processes, the lack of representation of mesoscale processes in coarse-resolution climate models, and the effect of natural climate variability at multi-decadal time scales. However, some of the spread in results originates simply from the variety of scenarios for future climate change used to drive climate model simulations, which hampers the ability to make generalizations about predicted changes in extreme weather events. In this study, we present a meta-analysis of the literature on projected future extreme weather events in order to quantify expected changes in weather extremes as a function of a common metric of global mean temperature increases. We find that many extreme weather events are likely to be significantly affected by global warming. In particular, our analysis indicates that the overall frequency of global tropical cyclones could decrease with global warming but that the intensity of these storms, as well as the frequency of the most intense cyclones could increase, particularly in the northwestern Pacific basin. We also found increases in the intensity of South Asian monsoonal rainfall, the frequency of global heavy precipitation events, the number of North American severe thunderstorm days, North American drought conditions, and European heatwaves, with rising global mean temperatures. In addition, the periodicity of the El Niño–Southern Oscillation may decrease, which could, in itself, influence extreme weather frequency in many areas of the climate system.     

Change in Extreme Climate Events over China Based on CMIP5

The changes in a selection of extreme climate indices(maximum of daily maximum temperature(TXx),minimum of daily minimum temperature(TNn),annual total precipitation when the daily precipitation exceeds the 95th percentile of wet-day precipitation(very wet days,R95p),and the maximum number of consecutive days with less than 1 mm of precipitation(consecutive dry days,CDD))were projected using multi-model results from phase 5 of the Coupled Model Intercomparison Project in the early,middle,and latter parts of the 21st century under different Representative Concentration Pathway(RCP)emissions scenarios.The results suggest that TXx and TNn will increase in the future and,moreover,the increases of TNn under all RCPs are larger than those of TXx.R95p is projected to increase and CDD to decrease significantly.The changes in TXx,TNn,R95p,and CDD in eight sub-regions of China are different in the three periods of the 21st century,and the ranges of change for the four indices under the higher emissions scenario are projected to be larger than those under the lower emissions scenario.The multi-model simulations show remarkable consistency in their projection of the extreme temperature indices,but poor consistency with respect to the extreme precipitation indices.More substantial inconsistency is found in those regions where high and low temperatures are likely to happen for TXx and TNn,respectively.For extreme precipitation events(R95p),greater uncertainty appears in most of the southern regions,while for drought events(CDD)it appears in the basins of Xinjiang.The uncertainty in the future changes of the extreme climate indices increases with the increasing severity of the emissions scenario.     

CMIP5全球气候模式对上海极端气温和降水的情景预估

基于国际耦合模式比较计划第五阶段(Coupled Model Intercomparison Project Phase 5,以下简称CMIP5)28个模式的数值模拟结果和1981~2010年华东和上海气温和降水观测数据,评估了该28个气候模式对华东和上海气温和降水的模拟能力,并预估了RCP4.5(Representative Concentration Pathway 4.5)情景下上海2021~2030年极端气温和降水气候的变化趋势和不确定性。结果表明:与观测值相比,模式对华东和上海年平均气温的模拟大多均值偏高、方差偏低;对年总降水量的模拟大多均值偏高,但方差以华东偏高、上海偏低为主;26个模式的气温变化趋势和12个模式的降水变化趋势与观测值相同。选出8个模式的预估结果表明:与2001~2010年相比,2021~2030年上海冬天极端低温的出现日数(冷夜日数)呈减少趋势,不确定性最小;夏天暖夜日数呈增加的趋势,不确定性较小;其他极端气温事件的变化趋势则存在较大的不确定性,冷夜指标的不确定性最大。强降水发生日数和强降水的强度都呈现增加的趋势,且不确定性较小。     

Projected changes in drought occurrence under future global warming from multi-model,multi-scenario,IPCC AR4 simulations

Recent and potential future increases in global temperatures are likely to be associated with impacts on the hydrologic cycle, including changes to precipitation and increases in extreme events such as droughts. We analyze changes in drought occurrence using soil moisture data for the SRES B1, A1B and A2 future climate scenarios relative to the PICNTRL pre-industrial control and 20C3M twentieth century simulations from eight AOGCMs that participated in the IPCC AR4. Comparison with observation forced land surface model estimates indicates that the models do reasonably well at replicating our best estimates of twentieth century, large scale drought occurrence, although the frequency of long-term (more than 12-month duration) droughts are over-estimated. Under the future projections, the models show decreases in soil moisture globally for all scenarios with a corresponding doubling of the spatial extent of severe soil moisture deficits and frequency of short-term (4–6-month duration) droughts from the mid-twentieth century to the end of the twenty-first. Long-term droughts become three times more common. Regionally, the Mediterranean, west African, central Asian and central American regions show large increases most notably for long-term frequencies as do mid-latitude North American regions but with larger variation between scenarios. In general, changes under the higher emission scenarios, A1B and A2 are the greatest, and despite following a reduced emissions pathway relative to the present day, the B1 scenario shows smaller but still substantial increases in drought, globally and for most regions. Increases in drought are driven primarily by reductions in precipitation with increased evaporation from higher temperatures modulating the changes. In some regions, increases in precipitation are offset by increased evaporation. Although the predicted future changes in drought occurrence are essentially monotonic increasing globally and in many regions, they are generally not statistically different from contemporary climate (as estimated from the 1961–1990 period of the 20C3M simulations) or natural variability (as estimated from the PICNTRL simulations) for multiple decades, in contrast to primary climate variables, such as global mean surface air temperature and precipitation. On the other hand, changes in annual and seasonal means of terrestrial hydrologic variables, such as evaporation and soil moisture, are essentially undetectable within the twenty-first century. Changes in the extremes of climate and their hydrological impacts may therefore be more detectable than changes in their means.     

2009/2010年冬季云南干旱的进一步研究——前期土壤湿度影响的数值模拟

采用新一代中尺度数值模式WRFv3.2版本,模拟研究了前期(秋季)土壤湿度异常对云南冬季降水的影响。数值模拟试验结果和一系列分析清楚表明,前期(秋季)土壤湿度的异常偏低,会导致云南地区冬季(12月1日~2月28日)降水的显著减少;前期土壤湿度减少一半,可以使云南冬季的降水量平均减少30%以上,小部分区域减少达50%以上,影响十分明显。大气环流及其主要参量模拟结果的对比分析清楚表明,持续的西偏北气流和干气团的控制以及云南地区大气散度场和垂直运动场等的异常是导致降水量减少的直接原因。对降水过程的分析表明,前期土壤湿度减少对降水过程的频次和发生时间的影响较小,但对各次过程的降水强度影响明显。这是前期土壤湿度减少所导致的包括区域性蒸发量和热通量等大气物理过程的改变决定的。本研究数值模拟结果与关于区域性土壤湿度异常影响机理的已有结论基本一致。     

未来情景下南水北调中线工程水源区极端降水分布特征

利用南水北调中线工程水源区9个气象站点1961-2008年的日降水资料和IPCC第四次评估报告多模式数据结果,抽取逐年的最大日降水量序列样本,运用广义极值分布（GEV）和广义帕累托分布 (GPD）两种极值统计模型对样本进行拟合,遴选出描述流域最大日降水量分布规律的最优概率模型,推算重现期对应的降水量值,并预估该流域极端降水事件在未来气候变化情景下的响应。研究表明：南水北调中线工程水源区降水极值均符合GEV和GPD分布,但GPD模型更适合用于描述该流域降水极值分布;未来气候变化情景下用GPD分布拟合的降水极值优于使用GEV分布;A2情景下极端降水事件的发生将更频繁、更强烈,A1B情景下次之,B1情景下相对较小,表明未来高排放气候情景对极端降水事件的影响比中、低排放情景大。     

A Review of Climate Change Attribution Studies

This paper reviews recent progress in climate change attribution studies. The focus is on the attribution of observed long-term changes in surface temperature, precipitation, circulation, and extremes, as well as that of specific extreme weather and climate events. Based on new methods and better models and observations, the latest studies further verify the conclusions on climate change attribution in the IPCC AR5, and enrich the evidence for anthropogenic influences on weather and climate variables and extremes. The uncertainty of global temperature change attributable to anthropogenic forcings lies in the considerable uncertainty of estimated total radiative forcing due to aerosols, while the uncertainty of precipitation change attribution arises from the limitations of observation and model simulations along with influences from large internal variability. In terms of extreme weather and climate events, it is clear that attribution studies have provided important new insights into the changes in the intensity or frequency of some of these events caused by anthropogenic climate change. The framing of the research question, the methods selected, and the model and statistical methods used all have influences on the results and conclusions drawn in an event attribution study. Overall, attribution studies in China remain inadequate because of limited research focus and the complexity of the monsoon climate in East Asia. Attribution research in China has focused mainly on changes or events related to temperature, such as the attribution of changes in mean and extreme temperature and individual heat wave events. Some progress has also been made regarding the pattern of changes in precipitation and individual extreme rainfall events in China. Nonetheless, gaps remain with respect to the attribution of changes in extreme precipitation, circulation, and drought, as well as to the event attribution such as those related to drought and tropical cyclones. It can be expected that, with the continual development of climate models, ongoing improvements to data, and the introduction of new methods in the future, climate change attribution research will develop accordingly. Additionally, further improvement in climate change attribution will facilitate the development of operational attribution systems for extreme events, as well as attribution studies of climate change impacts.     

不同升温情景下中国东北地区平均气候和极端气候事件变化预估

利用区域气候模式RegCM4的逐日气温和降水资料,预估1.5℃和2.0℃升温情景下,东北地区平均气候和极端气候事件的变化。结果表明：RCP4.5排放情景下,模式预计在2030年和2044年左右稳定达到1.5℃和2.0℃升温;两种升温情景下,东北地区气温、积温、生长季长度均呈增加趋势,且增幅随着升温阈值的升高而增加;1.5℃升温情景下,年平均气温增幅为1.19℃,年平均降水距平百分率增幅为5.78%,积温增加247.1℃·d,生长季长度延长7.0 d;2.0℃升温情景下气温、积温、生长季长度增幅较1.5℃升温情景下显著,但是年和四季降水普遍减少,年降水距平百分率减小1.96%。两种升温情景下,极端高温事件显著增加,极端低温事件显著减少,极端降水事件普遍增加。霜冻日数、结冰日数均呈显著减少趋势,热浪持续指数呈显著增加趋势;未来东北地区降水极端性增强,不仅单次降水过程的量级增大,极端降水过程的量级也明显增大,随着升温阈值的增大,极端降水的强度也逐渐增大。     

Changes in temperature and precipitation extremes in the CMIP5 ensemble

Twenty-year temperature and precipitation extremes and their projected future changes are evaluated in an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), updating a similar study based on the CMIP3 ensemble. The projected changes are documented for three radiative forcing scenarios. The performance of the CMIP5 models in simulating 20-year temperature and precipitation extremes is comparable to that of the CMIP3 ensemble. The models simulate late 20th century warm extremes reasonably well, compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes. Simulated late 20th century precipitation extremes are plausible in the extratropics but uncertainty in extreme precipitation in the tropics and subtropics remains very large, both in the models and the observationally-constrained datasets. Consistent with CMIP3 results, CMIP5 cold extremes generally warm faster than warm extremes, mainly in regions where snow and sea-ice retreat with global warming. There are tropical and subtropical regions where warming rates of warm extremes exceed those of cold extremes. Relative changes in the intensity of precipitation extremes generally exceed relative changes in annual mean precipitation. The corresponding waiting times for late 20th century extreme precipitation events are reduced almost everywhere, except for a few subtropical regions. The CMIP5 planetary sensitivity in extreme precipitation is about 6 %/°C, with generally lower values over extratropical land.     

气候变化背景下中国滑坡灾害人口风险评估

中国是世界上滑坡灾害造成人口伤亡较严重的国家.受气候变化影响,极端降水频率与强度的增加会提高滑坡灾害的人口风险.文中将不同RCPs情景多个模式的未来降水数据和SSPs情景下的未来人口数据相结合,构建滑坡灾害人口风险评估模型,评估气候变化背景下的中国滑坡灾害人口风险.研究发现,气候变化下中国滑坡灾害的危险性呈上升趋势,预...     

自动雨量站资料在地质灾害监测中的应用

通过对滑坡、泥石流地质灾害发生地区地质条件、外在因素、前期降水的影响等分析研究,设计了滑坡、泥石流地质灾害等级判别标准,并研制了预报方法。利用ArcGIS Engine、MICAPS等语言工具开发了滑坡、泥石流预报系统,使用前期数据库中自动气象站逐小时雨量和本地区的逐日降水影响系数,以及地质灾害气象发生等级条件进行计算,制作地质灾害发生等级预报。该研究工作为各地防治地质灾害提供了帮助和指导。     

全球升温1.5℃和2.0℃情景下澜沧江流域极端降水的变化特征

澜沧江是我国为数不多的跨境河流,流域内多发暴雨、洪水灾害,因此定量、科学地评估澜沧江流域未来全球升温情景下极端降水的变化特征,能够为澜沧江-湄公河沿线国家共同管理流域水资源和抵御自然灾害提供一定的科学指导。文中基于部门间影响模式比较计划（ISI-MIP）下5个全球气候模式降水数据,通过偏差校正增强其在澜沧江流域极端降水的模拟能力,使用降水强度、日最大降水量和强降水量等9个指标评价未来全球升温1.5℃和2.0℃下澜沧江流域极端降水的变化情况,并对结果的不确定性和可信度进行研究,得出以下主要结论：随着全球温度的升高,澜沧江流域年降水和极端降水均呈现增大趋势,其中极强降水量（R99p）升幅最大,升温1.5℃和2.0℃下升幅分别为37%和75%;相对于基准期,全球升温2.0℃下各极端降水指数增幅明显大于升温1.5℃,前者升幅甚至超出后者一倍;未来全球升温情景下,澜沧江流域湿季会变得更湿润,而干季则会更干燥;澜沧江流域降水集中程度会增大,使得流域内洪涝灾害发生的风险增大;ISI-MIP气候模式对澜沧江流域未来极端降水模拟存在较大不确定性,升温2.0℃较升温1.5℃情景下不确定性更大,但相对于基准期,前者极端降水增大的可信度更高。     

暴雨型滑坡灾害因素分析及预测试验研究

针对暴雨型滑坡灾害预报预警服务的需要, 以江西为研究区域, 从暴雨型滑坡灾害形成机理及预测理论入手, 通过8个滑坡灾害易发点的监测试验, 系统地研究了大气降水对地下水位、孔隙水压力、滑坡土体应力及滑坡稳定性的影响, 探讨了植被覆盖与滑坡的关系。结果表明:滑坡稳定系数与降水量有较好的负相关关系, 当降水量增大时, 滑坡稳定系数减小, 且略滞后于降水量的峰值; 在其他因素一定的条件下, 地下水位升高, 滑坡稳定系数直线下降。经计算得到8个试验点促使滑坡复活的临界指标是24 h降水量为60~203 mm, 值域的变化与滑坡体的土壤结构、力学性质、植被覆盖程度和降水强度的时空分布等因素有关。     

南京过去100年极端日降水量模拟研究

在南京过去100年日降水资料的基础上,利用极值理论中的区组模型和阈值模型分析了极端日降水分布特征.首先通过广义极值(GEV)模型模拟了日降水的年极值序列(AMDR),用极大似然估计(MLE)方法计算了模型的参数,并借助轮廓似然函数估计出参数的精确误差区间,同时采用4种较直观的诊断图形对模型的合理性进行全面评估,结果表明Frechet是区组模型中最适合描述极端日降水分布特征的函数.其次,将日降水序列分3种情景构建极值分布的阈值模型(GPD),考察了观测数据的规模对应用该模型的限制,重点讨论了如何针对给定观测样本选择合适的阈值收集极值信息.分析结果认为,长度不小于50年的气候序列,采用24 mm的日降水量作为临界阈值均能进行GPD分析.该阈值处于年降水序列第91个百分位附近,即对目前长度为50年左右的日观测资料,第91个百分位点以上的数据基本能满足GPD研究的需要.另外,根据GEV和GPD对未来极端降水重现水平的推断情况,GPD预测值的置信区间要比GEV的窄,极值推断的不确定性相对也较小,更适合用于研究中国目前规模不大的气候资料.最后,对GPD模型的形状参数和尺度参数进行变换,分别引入描述线性变化的动态变量,分析降水序列中潜在的变异行为对极值理论应用的影响.这种变异包括降水序列中长期的均值变化及百分位变化,从模拟结果看,暂未发现资料变异行为对极值分析产生显著于扰.     

Uncertainty in the Specification of Surface Characteristics, Part ii: Hierarchy of Interaction-Explicit Statistical Analysis

The uncertainty in the specification of surface characteristics in soil-vegetation- atmosphere-transfer (SVAT) schemes within planetary boundary-layer (PBL) or mesoscale models is addressed. The hypothesis to be tested is whether the errors in the specification of the individual parameters are accumulative or whether they tend to balance each other in the overall sense for the system. A hierarchy of statistical applications is developed: classical one-at-a-time (OAT) approach, level 1; linear analysis of variance (ANOVA), level 1.5; fractional factorial (FF), or level 2; two-factor interaction (TFI) technique, or level 2.5; and a non-linear response surface methodology (RSM), or level 3. Using the First ISLSCP Field Experiment (FIFE) observations for June 6, 1987 as the initial condition for a SVAT scheme dynamically coupled to a PBL model, the interactions between uncertainty errors are analyzed. A secondary objective addresses the temporal changes in the uncertainty pattern using data for morning, afternoon, and evening conditions. It is found that the outcome from the level 1 OAT-like studies can be considered as the limiting uncertainty values for the majority of mesoscale cases. From the higher-level analyses, it is concluded that for most of the moderate surface scenarios, the effective uncertainty from the individual parameters is balanced and thus lowered. However, for the extreme cases, such as near wilting or saturation soil moisture, the uncertainties add up synergistically and these effects can be even greater than those from the outcomes of the OAT-like studies. Thus, parameter uncertainty cannot be simply related to its deviation alone, but is also dependent on other parameter settings. Also, from the temporal changes in the interaction pattern studies, it is found that, for the morning case soil texture is the important parameter, for afternoon vegetation parameters are crucial, while for the evening case soil moisture is capable of propagating maximum uncertainty in the SVAT processes. Finally, a generic hypothesis is presented that an appropriate question for analysis has to be rephrased from the previous 'which parameters are significant?’ to 'what scenarios make a particular parameter significant?’     

Future cereal production in China: The interaction of climate change,water availability and socio-economic scenarios

Food production in China is a fundamental component of the national economy and driver of agricultural policy. Sustaining and increasing output to meet growing demand faces significant challenges including climate change, increasing population, agricultural land loss and competing demands for water. Recent warming in China is projected to accelerate by climate models with associated changes in precipitation and frequency of extreme events. How changes in cereal production and water availability due to climate change will interact with other socio-economic pressures is poorly understood. By linking crop and water simulation models and two scenarios of climate (derived from the Regional Climate Model PRECIS) and socio-economic change (downscaled from IPCC SRES A2 and B2) we demonstrate that by the 2040s the absolute effects of climate change are relatively modest. The interactive effects of other drivers are negative, leading to decreases in total production of ?18% (A2) and ?9% (B2). Outcomes are highly dependent on climate scenario, socio-economic development pathway and the effects of CO₂ fertilization on crop yields which may almost totally offset the decreases in production. We find that water availability plays a significant limiting role on future cereal production, due to the combined effects of higher crop water requirements (due to climate change) and increasing demand for non-agricultural use of water (due to socio-economic development). Without adaptation, per capita cereal production falls in all cases, by up to 40% of the current baseline.By simulating the effects of three adaptation scenarios we show that for these future scenarios China is able to maintain per capita cereal production, given reasonable assumptions about policies on land and water management and progress in agricultural technology. Our results are optimistic because PRECIS simulates much wetter conditions than a multi-model average, the CO₂ crop yield response function is highly uncertain and the effects of extreme events on crop growth and water availability are likely to be underestimated.     

Which downscaled rainfall data for climate change impact studies in urban areas? Review of current approaches and trends

Changes in extreme precipitation should be one of the primary impacts of climate change (CC) in urban areas. To assess these impacts, rainfall data from climate models are commonly used. The main goal of this paper is to report on the state of knowledge and recent works on the study of CC impacts with a focus on urban areas, in order to produce an integrated review of various approaches to which future studies can then be compared or constructed. Model output statistics (MOS) methods are increasingly used in the literature to study the impacts of CC in urban settings. A review of previous works highlights the non-stationarity nature of future climate data, underscoring the need to revise urban drainage system design criteria. A comparison of these studies is made difficult, however, by the numerous sources of uncertainty arising from a plethora of assumptions, scenarios, and modeling options. All the methods used do, however, predict increased extreme precipitation in the future, suggesting potential risks of combined sewer overflow frequencies, flooding, and back-up in existing sewer systems in urban areas. Future studies must quantify more accurately the different sources of uncertainty by improving downscaling and correction methods. New research is necessary to improve the data validation process, an aspect that is seldom reported in the literature. Finally, the potential application of non-stationarity conditions into generalized extreme value (GEV) distribution should be assessed more closely, which will require close collaboration between engineers, hydrologists, statisticians, and climatologists, thus contributing to the ongoing reflection on this issue of social concern.     

FUTURE CHANGE OF PRECIPITATION EXTREMES OVER THE PEARL RIVER BASIN FROM REGIONAL CLIMATE MODELS

Based on RegCM4, a climate model system, we simulated the distribution of the present climate (1961-1990) and the future climate (2010-2099), under emission scenarios of RCPs over the whole Pearl River Basin. From the climate parameters, a set of mean precipitation, wet day frequency, and mean wet day intensity and several precipitation percentiles are used to assess the expected changes in daily precipitation characteristics for the 21st century. Meanwhile the return values of precipitation intensity with an average return of 5, 10, 20, and 50 years are also used to assess the expected changes in precipitation extremes events in this study. The structure of the change across the precipitation distribution is very coherent between RCP4.5 and RCP8.5. The annual, spring and winter average precipitation decreases while the summer and autumn average precipitation increases. The basic diagnostics of precipitation show that the frequency of precipitation is projected to decrease but the intensity is projected to increase. The wet day percentiles (q90 and q95) also increase, indicating that precipitation extremes intensity will increase in the future. Meanwhile, the 5-year return value tends to increase by 30%-45% in the basins of Liujiang River, Red Water River, Guihe River and Pearl River Delta region, where the 5-year return value of future climate corresponds to the 8- to 10-year return value of the present climate, and the 50-year return value corresponds to the 100-year return value of the present climate over the Pearl River Delta region in the 2080s under RCP8.5, which indicates that the warming environment will give rise to changes in the intensity and frequency of extreme precipitation events.     

Impacts of extreme precipitation on tree plantation carbon cycle

Extreme precipitation events are expected to increase in frequency and magnitude in future due to global warming, but relevant impacts on tree plantation ecosystem carbon cycle are unknown. In this study, we use an atmosphere–vegetation interaction model (AVIM2) to estimate the likely impacts of extreme precipitation events on carbon fluxes and carbon stocks of a tree plantation in south China. Our results indicate that shifting from moderate precipitation events to extreme precipitation events whilst keeping monthly precipitation unchanged could decrease the tree plantation carbon accumulation. Tree plantation net primary productivity, net ecosystem productivity, soil carbon stock and vegetation carbon stock could decrease by 4.2, 28, 4.3 and 1.4 % during the studying period of 1962–2004, respectively. Though reductions in net primary productivity and net ecosystem productivity are relatively smaller than their annual variations, our sensitivity test shows that the tree plantation carbon stock could decrease by 3.3 % if the assumed extreme precipitation regime lasts for 500 years. Observed and simulated gross primary productivity, ecosystem respiration and net ecosystem productivity have significant positive correlation with soil water content (SWC), especially the deep SWC. The mechanism for the extreme precipitation effect is that the increase in extreme precipitation events will cause SWC to decrease, consequently, reducing carbon fluxes and stocks.