Postprocessing of simulated precipitation for impact research in West Africa. Part I: model output statistics for monthly data

Rainfall represents an important factor in agriculture and food security, particularly, in the low latitudes. Climatological and hydrological studies which attempt to diagnose the hydrological cycle, require high-quality precipitation data. In West Africa, like in many parts of the world, the density of observational data is low and climate models are needed in order to perform homogeneous and complete data sets. However, climate models tend to produce systematic errors, especially, in terms of rainfall and cloud processes, which are usually approximated by physical parameterizations. In this study, a 25-year climatology of monthly precipitation in West Africa is presented, derived from a regional climate model simulation, and evaluated with respect to observational data. It is found that the model systematically underestimates the rainfall amount and variability and does not capture some details of the seasonal cycle in sub-Saharan West Africa. Thus, in its present form the precipitation climatology is not appropriate to draw a realistic picture of the hydrological cycle in West Africa nor to serve as input data for impact research. Therefore, a statistical model is developed in order to adjust the simulated rainfall data to the characteristics of observed precipitation. Assuming that the regional climate model is much more reliable in terms of atmospheric circulation and thermodynamics, model output statistics is used to correct simulated rainfall by means of other simulated parameters of the near-surface climate like temperature, sea level pressure and wind components. Monthly data is adjusted by a cross-validated multiple regression model. The resulting adjusted rainfall climatology reveals a substantial improvement in terms of the model deficiencies mentioned above. In part II of this publication, the characteristics of simulated daily precipitation is adapted to station data by applying a weather generator. Once the postprocessing approach is trained, it can be extrapolated to simulation periods, for which observational data do not exist like for instance future climate.     

Downscaling daily precipitation time series using a combined circulation- and regression-based approach

The aim of this paper is to introduce a new conditional statistical model for generating daily precipitation time series. The generated daily precipitation can thus be used for climate change impact studies, e.g., crop production, rainfall–runoff, and other water-related processes. It is a stochastic model that links local rainfall events to a continuous atmospheric predictor, moisture flux, in addition to classified atmospheric circulation patterns. The coupled moisture flux is proved to be capable of capturing continuous property of climate system and providing extra information to determine rainfall probability and rainfall amount. The application was made to simultaneously downscale daily precipitation at multiple sites within the Rhine River basin. The results show that the model can well reproduce statistical properties of daily precipitation time series. Especially for extreme rainfall events, the model is thought to better reflect rainfall variability compared to the pure CP-based downscaling approach.     

A climate version of the regional atmospheric modeling system

Summary  The Regional Atmospheric Modeling System (RAMS) has been widely used to simulate relatively short-term atmospheric processes. To perform full-year to multi-year model integrations, a climate version of RAMS (ClimRAMS) has been developed, and is used to simulate diurnal, seasonal, and annual cycles of atmospheric and hydrologic variables and interactions within the central United States during 1989. The model simulation uses a 200-km grid covering the conterminous United States, and a nested, 50-km grid covering the Great Plains and Rocky Mountain states of Kansas, Nebraska, South Dakota, Wyoming, and Colorado. The model’s lateral boundary conditions are forced by six-hourly NCEP reanalysis products. ClimRAMS includes simplified precipitation and radiation sub-models, and representations that describe the seasonal evolution of vegetation-related parameters. In addition, ClimRAMS can use all of the general RAMS capabilities, like its more complex radiation sub-models, and explicit cloud and precipitation microphysics schemes. Thus, together with its nonhydrostatic and fully-interactive telescoping-grid capabilities, ClimRAMS can be applied to a wide variety of problems. Because of non-linear interactions between the land surface and atmosphere, simulating the observed climate requires simulating the observed diurnal, synoptic, and seasonal cycles. While previous regional climate modeling studies have demonstrated their ability to simulate the seasonal cycles through comparison with observed monthly-mean temperature and precipitation data sets, this study demonstrates that a regional climate model can also capture observed diurnal and synoptic variability. Observed values of daily precipitation and maximum and minimum screen-height air temperature are used to demonstrate this ability. Received September 27, 1999 Revised December 11, 1999     

A Climate Version of the Regional Atmospheric Modeling System

Summary The Regional Atmospheric Modeling System (RAMS) has been widely used to simulate relatively short-term atmospheric processes. To perform full-year to multi-year model integrations, a climate version of RAMS (ClimRAMS) has been developed, and is used to simulate diurnal, seasonal, and annual cycles of atmospheric and hydrologic variables and interactions within the central United States during 1989. The model simulation uses a 200-km grid covering the conterminous United States, and a nested, 50-km grid covering the Great Plains and Rocky Mountain states of Kansas, Nebraska, South Dakota, Wyoming, and Colorado. The model’s lateral boundary conditions are forced by six-hourly NCEP reanalysis products. ClimRAMS includes simplified precipitation and radiation sub-models, and representations that describe the seasonal evolution of vegetation-related parameters. In addition, ClimRAMS can use all of the general RAMS capabilities, like its more complex radiation sub-models, and explicit cloud and precipitation microphysics schemes. Thus, together with its nonhydrostatic and fully-interactive telescoping-grid capabilities, ClimRAMS can be applied to a wide variety of problems. Because of non-linear interactions between the land surface and atmosphere, simulating the observed climate requires simulating the observed diurnal, synoptic, and seasonal cycles. While previous regional climate modeling studies have demonstrated their ability to simulate the seasonal cycles through comparison with observed monthly-mean temperature and precipitation data sets, this study demonstrates that a regional climate model can also capture observed diurnal and synoptic variability. Observed values of daily precipitation and maximum and minimum screen-height air temperature are used to demonstrate this ability. Received September 27, 1999 Revised December 11, 1999     

How well are daily intense rainfall events captured by current climate models over Africa?

The ability of state-of-the-art climate models to capture the mean spatial and temporal characteristics of daily intense rainfall events over Africa is evaluated by analyzing regional climate model (RCM) simulations at 90- and 30-km along with output from four atmospheric general circulation models (AGCMs) and coupled atmosphere–ocean general circulation models (AOGCMs) of the Climate Model Intercomparison Project 5. Daily intense rainfall events are extracted at grid point scale using a 95th percentile threshold approach applied to all rainy days (i.e., daily rainfall ≥1 mm day^?1) over the 1998–2008 period for which two satellite-derived precipitation products are available. Both RCM simulations provide similar results. They accurately capture the spatial and temporal characteristics of intense events, while they tend to overestimate their number and underestimate their intensity. The skill of AGCMs and AOGCMs is generally similar over the African continent and similar to previous global climate model generations. The majority of the AGCMs and AOGCMs greatly overestimate the frequency of intense events, particularly in the tropics, generally fail at simulating the observed intensity, and systematically overestimate their spatial coverage. The RCM performs at least as well as the most accurate global climate model, demonstrating a clear added value to general circulation model simulations and the usefulness of regional modeling for investigating the physics leading to intense events and their change under global warming.     

NCEP/NCAR再分析资料所揭示的全球季风降水变化

大气模式是研究气候变化的重要工具,当前的大气模式在模拟季风降水时均存在较大偏差,目前尚不清楚该偏差是来自模式环流场还是模式物理过程.再分析资料由于同化了各类观测和卫星资料,其大气环流近似可被视作是“真实”的.再分析资料中的降水场是在基本真实的环流场强迫下,由当前最先进的数值预报模式计算输出的.因此,再分析资料的降水场能...     

Effect of land model ensemble versus coupled model ensemble on the simulation of precipitation climatology and variability

Through a series of model simulations with an atmospheric general circulation model coupled to three different land surface models, this study investigates the impacts of land model ensembles and coupled model ensemble on precipitation simulation. It is found that coupling an ensemble of land models to an atmospheric model has a very minor impact on the improvement of precipitation climatology and variability, but a simple ensemble average of the precipitation from three individually coupled land-atmosphere models produces better results, especially for precipitation variability. The generally weak impact of land processes on precipitation should be the main reason that the land model ensembles do not improve precipitation simulation. However, if there are big biases in the land surface model or land surface data set, correcting them could improve the simulated climate, especially for well-constrained regional climate simulations.     

A comprehensive model inter-comparison study investigating the water budget during the BALTEX-PIDCAP period

Summary A comparison of 8 regional atmospheric model systems was carried out for a three-month late summer/early autumn period in 1995 over the Baltic Sea and its catchment area. All models were configured on a common grid using similar surface and lateral boundary conditions, and ran in either data assimilation mode (short term forecasts plus data assimilation), forecast mode (short term forecasts initialised daily with analyses) or climate mode (no re-initialisation of model interior during entire simulation period). Model results presented in this paper were generally post processed as daily averaged quantities, separate for land and sea areas when relevant. Post processed output was compared against available analyses or observations of cloud cover, precipitation, vertically integrated atmospheric specific humidity, runoff, surface radiation and near surface synoptic observations. The definition of a common grid and lateral forcing resulted in a high degree of agreement among the participating model results for most cases. Models operated in climate mode generally displayed slightly larger deviations from the observations than the data assimilation or forecast mode integration, but in all cases synoptic events were well captured. Correspondence to near surface synoptic quantities was good. Significant disagreement between model results was shown in particular for cloud cover and the radiative properties, average precipitation and runoff. Problems with choosing appropriate initial soil moisture conditions from a common initial soil moisture field resulted in a wide range of evaporation and sensible heat flux values during the first few weeks of the simulations, but better agreement was shown at later times. Received September 8, 2000 Revised April 3, 2001     

Simulating the diurnal cycle of rainfall in global climate models: resolution versus parameterization

The effects of horizontal resolution and the treatment of convection on simulation of the diurnal cycle of precipitation during boreal summer are analyzed in several innovative weather and climate model integrations. The simulations include: season-long integrations of the Non-hydrostatic Icosahedral Atmospheric Model (NICAM) with explicit clouds and convection; year-long integrations of the operational Integrated Forecast System (IFS) from the European Centre for Medium-range Weather Forecasts at three resolutions (125, 39 and 16 km); seasonal simulations of the same model at 10 km resolution; and seasonal simulations of the National Center for Atmospheric Research (NCAR) low-resolution climate model with and without an embedded two-dimensional cloud-resolving model in each grid box. NICAM with explicit convection simulates best the phase of the diurnal cycle, as well as many regional features such as rainfall triggered by advancing sea breezes or high topography. However, NICAM greatly overestimates mean rainfall and the magnitude of the diurnal cycle. Introduction of an embedded cloud model within the NCAR model significantly improves global statistics of the seasonal mean and diurnal cycle of rainfall, as well as many regional features. However, errors often remain larger than for the other higher-resolution models. Increasing resolution alone has little impact on the timing of daily rainfall in IFS with parameterized convection, yet the amplitude of the diurnal cycle does improve along with the representation of mean rainfall. Variations during the day in atmospheric prognostic fields appear quite similar among models, suggesting that the distinctive treatments of model physics account for the differences in representing the diurnal cycle of precipitation.     

Evaluation of HadCM2 and Direct Use of Daily GCM Data in Impact Assessment Studies

This paper investigates two important aspects of methods used to explore possible effects of climatic changes on agricultural productivity on regional spatial scales. First, an evaluation of precipitation and near surface air temperature in two successive versions of the Hadley Centre General Circulation Model (GCM) has been performed to consider to what extent GCMs are capable of simulating the mean and variability of local climates. This is explored by comparing the output of an individual GCM grid box with three station observations. Several ancillary issues associated with the comparisons of observations of daily precipitation and model output that affect the statistical results are also discussed. Finally, daily data from the control and sulphate runs of the latest Hadley Centre GCM (HadCM2) have been used directly as input to the CERES-Wheat model, and the modelled yield distribution is compared to that produced with the historical data series. Our results imply that for this particular grid box covering the study region in central France, the daily raw data from HadCM2 experiment can be used directly to assess the potential impact of the greenhouse gas and sulphate aerosol radiative induced forcings and the associated climatic change on average regional winter wheat production. On the other hand, less confidence should be placed on their use regarding the estimation of future agricultural risk and variability assessment. Furthermore, a possibly more severe methodological problem that has arisen from our study is the inability of CERES-Wheat to simulate the waterlogging effects of excessive soil water on crop growth and development. Finally, we assess the potential impact of changing climate on regional winter wheat production by using the daily data from the sulphate integration up to the end of the 21st century.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

Simulations of precipitation using NRCM and comparisons with satellite observations and CAM: annual cycle

The accurate representation of rainfall in models of global climate has been a challenging task for climate modelers owing to its small space and time scales. Quantifying this variability is important for comparing simulations of atmospheric behavior with real time observations. In this regard, this paper compares both the statistical and dynamically forced aspects of precipitation variability simulated by the high-resolution (36?km) Nested Regional Climate Model (NRCM), with satellite observations from the Tropical Rainfall Measuring Mission (TRMM) 3B42 dataset and simulations from the Community Atmosphere Model (CAM) at T85 spatial resolution. Six years of rainfall rate data (2000?C2005) from within the Tropics (30°S?C30°N) have been used in the analysis and results are presented in terms of long-term mean rain rates, amplitude and phase of the annual cycle and seasonal mean maps of precipitation. Our primary focus is on characterizing the annual cycle of rainfall over four land regions of the Tropics namely, the Indian Monsoon, the Amazon, Tropical Africa and the North American monsoon. The lower tropospheric circulation patterns are analyzed in both the observations and the models to identify possible causes for biases in the simulated precipitation. The 6-year mean precipitation simulated by both models show substantial biases throughout the global Tropics with NRCM/CAM systematically underestimating/overestimating rainfall almost everywhere. The seasonal march of rainfall across the equator, following the motion of the sun, is clearly seen in the harmonic vector maps. The timing of peak rainfall (phase) produced by NRCM is in closer agreement with the observations compared to CAM. However like the long-time mean, the magnitude of seasonal mean rainfall is greatly underestimated by NRCM throughout the Tropical land mass. Some of these regional biases can be attributed to erroneous circulation and moisture surpluses/deficits in the lower troposphere in both models. Overall, the results seem to indicate that employing a higher spatial resolution (36?km) does not significantly improve simulation of precipitation. We speculate that a combination of several physics parameterizations and lack of model tuning gives rise to the observed differences between NRCM and the observations.     

Downscaling extreme month-long anomalies in southern South America

We investigate the performance of one stretched-grid atmospheric global model, five different regional climate models and a statistical downscaling technique in simulating 3 months (January 1971, November 1986, July 1996) characterized by anomalous climate conditions in the southern La Plata Basin. Models were driven by reanalysis (ERA-40). The analysis has emphasized on the simulation of the precipitation over land and has provided a quantification of the biases of and scatter between the different regional simulations. Most but not all dynamical models underpredict precipitation amounts in south eastern South America during the three periods. Results suggest that models have regime dependence, performing better for some conditions than others. The models’ ensemble and the statistical technique succeed in reproducing the overall observed frequency of daily precipitation for all periods. But most models tend to underestimate the frequency of dry days and overestimate the amount of light rainfall days. The number of events with strong or heavy precipitation tends to be under simulated by the models.     

Daily precipitation intensity projected for the 21st century: seasonal changes over the Pyrenees

A set of climate parameters (mean precipitation, number of wet days, daily intensity, and number of days with more than 50 mm rainfall) and a quantile-based approach are used to assess the expected changes in daily precipitation characteristics over the Pyrenees predicted for the 21st century using a set of regional climate models (RCMs). The features of the geographic location and topography of the Pyrenees imply that the climate of the region is highly complex. The results point toward an intensification of extremes, with a generalized tendency toward increasing drought periods, an increasing trend in daily intensity, and an increasing contribution of intense events to total precipitation; however, the results are subject to substantial spatial and seasonal variability, mainly related to the Atlantic-Mediterranean gradient and the longitudinal disposition of the main axis of the range.     

The use of an index-based regression model for precipitation analysis on the Iberian peninsula

Summary  A large number of atmospheric circulation classification techniques have been developed in the investigation of synoptic controls on regional rainfall. Often the rationale is to aid efforts to downscale GCM output for the purpose of producing more confident climate change impact scenarios. Discrete weather typing techniques, although proven to be successful do not capture weather type intensity and within-type variability can often be high. In this study an objective indexing method, developed for Egypt and the British Isles area is applied to the Iberian peninsula. Air flow index values are then used as predictor variables in simple linear regression models to estimate monthly mean grid point rainfall amounts. Separate models are evaluated for the winter and summer halves of the year and also for surface and mid-tropospheric flow (500 hPa). The models are evaluated and compared indicating that the index values provide good estimation of rainfall but variability in performance between season and site is noted. Received February 10, 2000     

The influence of synoptic airflow on UK daily precipitation extremes. Part II: regional climate model and E-OBS data validation

We investigate how well the variability of extreme daily precipitation events across the United Kingdom is represented in a set of regional climate models and the E-OBS gridded data set. Instead of simply evaluating the climatologies of extreme precipitation measures, we develop an approach to validate the representation of physical mechanisms controlling extreme precipitation variability. In part I of this study we applied a statistical model to investigate the influence of the synoptic scale atmospheric circulation on extreme precipitation using observational rain gauge data. More specifically, airflow strength, direction and vorticity are used as predictors for the parameters of the generalised extreme value (GEV) distribution of local precipitation extremes. Here we employ this statistical model for our validation study. In a first step, the statistical model is calibrated against a gridded precipitation data set provided by the UK Met Office. In a second step, the same statistical model is calibrated against 14 ERA40 driven 25?km resolution RCMs from the ENSEMBLES project and the E-OBS gridded data set. Validation indices describing relevant physical mechanisms are derived from the statistical models for observations and RCMs and are compared using pattern standard deviation, pattern correlation and centered pattern root mean squared error as validation measures. The results for the different RCMs and E-OBS are visualised using Taylor diagrams. We show that the RCMs adequately simulate moderately extreme precipitation and the influence of airflow strength and vorticity on precipitation extremes, but show deficits in representing the influence of airflow direction. Also very rare extremes are misrepresented, but this result is afflicted with a high uncertainty. E-OBS shows considerable biases, in particular in regions of sparse data. The proposed approach might be used to validate other physical relationships in regional as well as global climate models.     

Downscaling daily precipitation over the Yellow River source region in China: a comparison of three statistical downscaling methods

Three statistical downscaling methods are compared with regard to their ability to downscale summer (June–September) daily precipitation at a network of 14 stations over the Yellow River source region from the NCEP/NCAR reanalysis data with the aim of constructing high-resolution regional precipitation scenarios for impact studies. The methods used are the Statistical Downscaling Model (SDSM), the Generalized LInear Model for daily CLIMate (GLIMCLIM), and the non-homogeneous Hidden Markov Model (NHMM). The methods are compared in terms of several statistics including spatial dependence, wet- and dry spell length distributions and inter-annual variability. In comparison with other two models, NHMM shows better performance in reproducing the spatial correlation structure, inter-annual variability and magnitude of the observed precipitation. However, it shows difficulty in reproducing observed wet- and dry spell length distributions at some stations. SDSM and GLIMCLIM showed better performance in reproducing the temporal dependence than NHMM. These models are also applied to derive future scenarios for six precipitation indices for the period 2046–2065 using the predictors from two global climate models (GCMs; CGCM3 and ECHAM5) under the IPCC SRES A2, A1B and B1scenarios. There is a strong consensus among two GCMs, three downscaling methods and three emission scenarios in the precipitation change signal. Under the future climate scenarios considered, all parts of the study region would experience increases in rainfall totals and extremes that are statistically significant at most stations. The magnitude of the projected changes is more intense for the SDSM than for other two models, which indicates that climate projection based on results from only one downscaling method should be interpreted with caution. The increase in the magnitude of rainfall totals and extremes is also accompanied by an increase in their inter-annual variability.     

Regional climate model projections for the State of Washington

Global climate models do not have sufficient spatial resolution to represent the atmospheric and land surface processes that determine the unique regional climate of the State of Washington. Regional climate models explicitly simulate the interactions between the large-scale weather patterns simulated by a global model and the local terrain. We have performed two 100-year regional climate simulations using the Weather Research and Forecasting (WRF) model developed at the National Center for Atmospheric Research (NCAR). One simulation is forced by the NCAR Community Climate System Model version 3 (CCSM3) and the second is forced by a simulation of the Max Plank Institute, Hamburg, global model (ECHAM5). The mesoscale simulations produce regional changes in snow cover, cloudiness, and circulation patterns associated with interactions between the large-scale climate change and the regional topography and land-water contrasts. These changes substantially alter the temperature and precipitation trends over the region relative to the global model result or statistical downscaling. To illustrate this effect, we analyze the changes from the current climate (1970–1999) to the mid twenty-first century (2030–2059). Changes in seasonal-mean temperature, precipitation, and snowpack are presented. Several climatological indices of extreme daily weather are also presented: precipitation intensity, fraction of precipitation occurring in extreme daily events, heat wave frequency, growing season length, and frequency of warm nights. Despite somewhat different changes in seasonal precipitation and temperature from the two regional simulations, consistent results for changes in snowpack and extreme precipitation are found in both simulations.     

On the capability of RegCM to capture extremes in long term regional climate simulation – comparison with the observations for Czech Republic

Summary We analyze daily precipitation and temperature extremes over the Czech Republic in a regional climate simulation for the 40-year period of 1961–2000 carried out with the RegCM3 regional climate model. The model is run at 45 km grid interval and is driven by NCEP/NCAR reanalysis lateral boundary conditions. Comparison with station data shows that the model performs reasonably well in simulating the frequency of daily precipitation events of medium to high intensity as well as the precipitation intensities (return levels) of long return periods, with the exception of mountain stations. While this is attributed mainly to the relatively coarse representation of topography across the area of the Czech Republic, the parameterization of convection can be another reason. The model underestimates daily maximum temperature (especially in the warm seasons) and as a result the occurrence of heat waves (high temperature episodes). The performance of the model improves in the simulation of daily minimum temperature and cold wave events. In order to apply this regional model to the simulation of extreme events over the complex terrain as for Czech Republic we recommend that a higher resolution is used in order to better describe the topography of the Czech Republic and that the daily maximum temperature bias is reduced.     

Numerical Experiments on the Spin-up Time for Seasonal-Scale Regional Climate Modeling

In this paper,the numerical experiments on the issue of spin-up time for seasonal-scale regional climate modeling were conducted with the newly Regional Climate Model (RegCM3),in the case of the abnormal climate event during the summer of 1998 in China.To test the effect of spin-up time on the regional climate simulation results for such abnormal climate event,a total of 11 experiments were performed with different spin-up time from 10 days to 6 months,respectively.The simulation results show that,for the meteorological variables in the atmosphere,the model would be running in"climate mode"after 4-8-day spin-up time,then, it is independent of the spin-up time basically,and the simulation errors are mainly caused by the model's failure in describing the atmospheric processes over the model domain.This verifies again that the regional climate modeling is indeed a lateral boundary condition problem as demonstrated by earlier research work. The simulated mean precipitation rate over each subregion is not sensitive to the spin-up time,but the precipitation scenario is somewhat different for the experiment with different spin-up time,which shows that there exists the uncertainty in the simulation to precipitation scenario,and such a uncertainty exhibits more over the areas where heavy rainfall happened.Generally,for monthly-scale precipitation simulation,a spin-up time of 1 month is enough,whereas a spin-up time of 2 months is better for seasonal-scale one. Furthermore,the relationship between the precipitation simulation error and the advancement/withdrawal of East Asian summer monsoon was analyzed.It is found that the variability of correlation coefficient for precipitation is more significant over the areas where the summer monsoon is predominant.Therefore,the model's capability in reproducing precipitation features is related to the heavy rainfall processes associated with the advancement/withdrawal of East Asian summer monsoon,which suggests that it is necessary to develop a more reliable parameterization scheme to capture the convective precipitation of heavy rainfall pro- cesses associated with the activities of East Asian summer monsoon,so as to improve the climate modeling over China.