The Common Principal Component Analyses of Multi-RCMs

Based on a 10-year simulation of six Regional Climate Models(RCMs) in phase II of the Regional Climate Model Inter-Comparison Project(RMIP) for Asia,the multivariate statistical method of common principal components(CPCs) is used to analyze and compare the spatiotemporal characteristics of temperature and precipitation simulated by multi-RCMs over China,including the mean climate states and their seasonal transition,the spatial distribution of interannual variability,and the interannual variation.CPC is an effective statistical tool for analyzing the results of different models.Compared with traditional statistical methods,CPC analyses provide a more complete statistical picture for observation and simulation results.The results of CPC analyses show that the climatological means and the characteristics of seasonal transition over China can be accurately simulated by RCMs.However,large biases exist in the interannual variation in certain years or for individual models.     

High-resolution simulations for Vietnam - methodology and evaluation of current climate

To assist the government of Vietnam in its efforts to better understand the impacts of climate change and prioritise its adaptation measures, dynamically downscaled climate change projections were produced across Vietnam. Two Regional Climate Models (RCMs) were used: CSIRO’s variable-resolution Conformal-Cubic Atmospheric Model (CCAM) and the limited-area model Regional Climate Model system version 4.2 (RegCM4.2). First, global CCAM simulations were completed using bias- and variance-corrected sea surface temperatures as well as sea ice concentrations from six Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models. This approach is different from other downscaling approaches as it does not use any atmospheric fields from the GCMs. The global CCAM simulations were then further downscaled to 10 km using CCAM and to 20 km using RegCM4.2. Evaluations of temperature and precipitation for the current climate (1980-2000) were completed using station data as well as various gridded observational datasets. The RCMs were able to reproduce reasonably well most of the important characteristics of observed spatial patterns and annual cycles of temperature. Average and minimum temperatures were well simulated (biases generally less than 1oC), while maximum temperatures had biases of around 1oC. For precipitation, although the RCMs captured the annual cycle, RegCM4.2 was too dry in Oct.-Nov. (-60% bias), while CCAM was too wet in Dec.- Mar. (130% bias). Both models were too dry in summer and too wet in winter (especially in northern Vietnam). The ability of the ensemble simulations to capture current climate increases confidence in the simulations of future climate.     

Time of emergence of anthropogenic warming signals in the Northeast Asia assessed from multi-regional climate models

Time of Emergence (ToE) is the time at which the signal of climate change emerges from the background noise of natural climate variability, and can provide useful information for climate change impacts and adaptations. This study examines future ToEs for daily maximum and minimum temperatures over the Northeast Asia using five Regional Climate Models (RCMs) simulations driven by single Global Climate Model (GCM) under two Representative Concentration Pathways (RCP) emission scenarios. Noise is defined based on the interannual variability during the present-day period (1981-2010) and warming signals in the future years (2021-2100) are compared against the noise in order to identify ToEs. Results show that ToEs of annual mean temperatures occur between 2030s and 2040s in RCMs, which essentially follow those of the driving GCM. This represents the dominant influence of GCM boundary forcing on RCM results in this region. ToEs of seasonal temperatures exhibit larger ranges from 2030s to 2090s. The seasonality of ToE is found to be determined majorly by noise amplitudes. The earliest ToE appears in autumn when the noise is smallest while the latest ToE occurs in winter when the noise is largest. The RCP4.5 scenario exhibits later emergence years than the RCP8.5 scenario by 5-35 years. The significant delay in ToEs by taking the lower emission scenario provides an important implication for climate change mitigation. Daily minimum temperatures tend to have earlier emergence than daily maximum temperature but with low confidence. It is also found that noise thresholds can strongly affect ToE years, i.e. larger noise threshold induces later emergence, indicating the importance of noise estimation in the ToE assessment.     

Uncertainties in the regional climate models simulations of South-Asian summer monsoon and climate change

The uncertainties in the regional climate models (RCMs) are evaluated by analyzing the driving global data of ERA40 reanalysis and ECHAM5 general circulation models, and the downscaled data of two RCMs (RegCM4 and PRECIS) over South-Asia for the present day simulation (1971–2000) of South-Asian summer monsoon. The differences between the observational datasets over South-Asia are also analyzed. The spatial and the quantitative analysis over the selected climatic regions of South-Asia for the mean climate and the inter-annual variability of temperature, precipitation and circulation show that the RCMs have systematic biases which are independent from different driving datasets and seems to come from the physics parameterization of the RCMs. The spatial gradients and topographically-induced structure of climate are generally captured and simulated values are within a few degrees of the observed values. The biases in the RCMs are not consistent with the biases in the driving fields and the models show similar spatial patterns after downscaling different global datasets. The annual cycle of temperature and rainfall is well simulated by the RCMs, however the RCMs are not able to capture the inter-annual variability. ECHAM5 is also downscaled for the future (2071–2100) climate under A1B emission scenario. The climate change signal is consistent between ECHAM5 and RCMs. There is warming over all the regions of South-Asia associated with increasing greenhouse gas concentrations and the increase in summer mean surface air temperature by the end of the century ranges from 2.5 to 5 °C, with maximum warming over north western parts of the domain and 30 % increase in rainfall over north eastern India, Bangladesh and Myanmar.     

Validation of precipitation over Japan during 1985–2004 simulated by three regional climate models and two multi-model ensemble means

We dynamically downscaled Japanese reanalysis data (JRA-25) for 60 regions of Japan using three regional climate models (RCMs): the Non-Hydrostatic Regional Climate Model (NHRCM), modified RAMS version 4.3 (NRAMS), and modified Weather Research and Forecasting model (TWRF). We validated their simulations of the precipitation climatology and interannual variations of summer and winter precipitation. We also validated precipitation for two multi-model ensemble means: the arithmetic ensemble mean (AEM) and an ensemble mean weighted according to model reliability. In the 60 regions NRAMS simulated both the winter and summer climatological precipitation better than JRA-25, and NHRCM simulated the wintertime precipitation better than JRA-25. TWRF, however, overestimated precipitation in the 60 regions in both the winter and summer, and NHRCM overestimated precipitation in the summer. The three RCMs simulated interannual variations, particularly summer precipitation, better than JRA-25. AEM simulated both climatological precipitation and interannual variations during the two seasons more realistically than JRA-25 and the three RCMs overall, but the best RCM was often superior to the AEM result. In contrast, the weighted ensemble mean skills were usually superior to those of the best RCM. Thus, both RCMs and multi-model ensemble means, especially multi-model ensemble means weighted according to model reliability, are powerful tools for simulating seasonal and interannual variability of precipitation in Japan under the current climate.     

Inter-Comparison of 10-year Precipitation Simulated by Several RCMs for Asia

In phase Ⅱ of the Regional Climate Model Inter-comparison Project (RMIP) for Asia, the regional climate has been simulated for July 1988 through December 1998 by five regional climate models and one global variable resolution model. Comparison of the 10-year simulated precipitation with the observations was carried out. The results show that most models have the capacity to reproduce the basic spatial pattern of precipitation for Asia, and the main rainbelt can be reproduced by most models, but there are distinctions in the location and the intensity. Most models overestimate the precipitation over most continental regions. Interannual variability of the precipitation can also be basically simulated, while differences exist between various models and the observations. The biases in the stream field are important reasons behind the simulation errors of the Regional Climate Models (RCMs). The cumulus scheme and land surface process have large influences on the precipitation simulation. Generally, the Grell cumulus scheme produces more precipitation than the Kuo scheme.     

Evaluation of an ensemble of regional climate model simulations over South America driven by the ERA-Interim reanalysis: model performance and uncertainties

The capability of a set of 7 coordinated regional climate model simulations performed in the framework of the CLARIS-LPB Project in reproducing the mean climate conditions over the South American continent has been evaluated. The model simulations were forced by the ERA-Interim reanalysis dataset for the period 1990–2008 on a grid resolution of 50 km, following the CORDEX protocol. The analysis was focused on evaluating the reliability of simulating mean precipitation and surface air temperature, which are the variables most commonly used for impact studies. Both the common features and the differences among individual models have been evaluated and compared against several observational datasets. In this study the ensemble bias and the degree of agreement among individual models have been quantified. The evaluation was focused on the seasonal means, the area-averaged annual cycles and the frequency distributions of monthly means over target sub-regions. Results show that the Regional Climate Model ensemble reproduces adequately well these features, with biases mostly within ±2 °C and ±20 % for temperature and precipitation, respectively. However, the multi-model ensemble depicts larger biases and larger uncertainty (as defined by the standard deviation of the models) over tropical regions compared with subtropical regions. Though some systematic biases were detected particularly over the La Plata Basin region, such as underestimation of rainfall during winter months and overestimation of temperature during summer months, every model shares a similar behavior and, consequently, the uncertainty in simulating current climate conditions is low. Every model is able to capture the variety in the shape of the frequency distribution for both temperature and precipitation along the South American continent. Differences among individual models and observations revealed the nature of individual model biases, showing either a shift in the distribution or an overestimation or underestimation of the range of variability.     

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.     

Present and future Laurentian Great Lakes hydroclimatic conditions as simulated by regional climate models with an emphasis on Lake Michigan-Huron

Regional climate modelling represents an appealing approach to projecting Great Lakes water supplies under a changing climate. In this study, we investigate the response of the Great Lakes Basin to increasing greenhouse gas and aerosols emissions using an ensemble of sixteen climate change simulations generated by three different Regional Climate Models (RCMs): CRCM4, HadRM3 and WRFG. Annual and monthly means of simulated hydro-meteorological variables that affect Great Lakes levels are first compared to observation-based estimates. The climate change signal is then assessed by computing differences between simulated future (2041–2070) and present (1971–1999) climates. Finally, an analysis of the annual minima and maxima of the Net Basin Supply (NBS), derived from the simulated NBS components, is conducted using Generalized Extreme Value distribution. Results reveal notable model differences in simulated water budget components throughout the year, especially for the lake evaporation component. These differences are reflected in the resulting NBS. Although uncertainties in observation-based estimates are quite large, our analysis indicates that all three RCMs tend to underestimate NBS in late summer and fall, which is related to biases in simulated runoff, lake evaporation, and over-lake precipitation. The climate change signal derived from the total ensemble mean indicates no change in future mean annual NBS. However, our analysis suggests an amplification of the NBS annual cycle and an intensification of the annual NBS minima in future climate. This emphasizes the need for an adaptive management of water to minimize potential negative implications associated with more severe and frequent NBS minima.     

Do weak AR4 models bias projections of future climate changes over Australia?

Regional climate projections using climate models commonly use an “all-model” ensemble based on data sets such as the Intergovernmental Panel on Climate Change’s (IPCC) 4th Assessment (AR4). Some regional assessments have omitted models based on specific criteria. We use a criteria based on the capacity of climate models to simulate the observed probability density function calculated using daily data, model-by-model and region-by-region for each of the AR4 models over Australia. We demonstrate that by omitting those climate models with relatively weak skill in simulating the observed probability density functions of maximum and minimum temperature and precipitation, different regional projections are obtained. Differences include: larger increases in the mean maximum and mean minimum temperatures, but smaller increases in the annual maximum and minimum temperatures. There is little impact on mean precipitation but the better models simulate a larger increase in the annual rainfall event combined with a larger decrease in the number of rain days. The weaker models bias the amount of mean warming towards lower increases, bias annual maximum temperatures to excessive warming and bias precipitation such that the amount of the annual rainfall event is under-estimated. We suggest that omitting weak models from regional scale estimates of future climate change helps clarify the nature and scale of the projected impacts of global warming.     

Evaluation of regional climate model simulations versus gridded observed and regional reanalysis products using a combined weighting scheme

This study presents a combined weighting scheme which contains five attributes that reflect accuracy of climate data, i.e. short-term (daily), mid-term (annual), and long-term (decadal) timescales, as well as spatial pattern, and extreme values, as simulated from Regional Climate Models (RCMs) with respect to observed and regional reanalysis products. Southern areas of Quebec and Ontario provinces in Canada are used for the study area. Three series of simulation from two different versions of the Canadian RCM (CRCM4.1.1, and CRCM4.2.3) are employed over 23?years from 1979 to 2001, driven by both NCEP and ERA40 global reanalysis products. One series of regional reanalysis dataset (i.e. NARR) over North America is also used as reference for comparison and validation purpose, as well as gridded historical observed daily data of precipitation and temperatures, both series have been beforehand interpolated on the CRCM 45-km grid resolution. Monthly weighting factors are calculated and then combined into four seasons to reflect seasonal variability of climate data accuracy. In addition, this study generates weight averaged references (WARs) with different weighting factors and ensemble size as new reference climate data set. The simulation results indicate that the NARR is in general superior to the CRCM simulated precipitation values, but the CRCM4.1.1 provides the highest weighting factors during the winter season. For minimum and maximum temperature, both the CRCM4.1.1 and the NARR products provide the highest weighting factors, respectively. The NARR provides more accurate short- and mid-term climate data, but the two versions of the CRCM provide more precise long-term data, spatial pattern and extreme events. Or study confirms also that the global reanalysis data (i.e. NCEP vs. ERA40) used as boundary conditions in the CRCM runs has non-negligible effects on the accuracy of CRCM simulated precipitation and temperature values. In addition, this study demonstrates that the proposed weighting factors reflect well all five attributes and the performances of weighted averaged references are better than that of the best single model. This study also found that the improvement of WARs’ performance is due to the reliability (accuracy) of RCMs rather than the ensemble size.     

对流允许尺度区域气候模拟的研究进展

与以往的区域气候模式相比,对流允许区域气候模式不再依赖于对流参数化方案,其精细的分辨率可以显式表示深对流过程,在夏季对流降水的日变化和极端降水事件模拟等方面具有明显增值能力,是区域气候模拟的发展方向。对现有的对流允许尺度区域气候模拟研究进行了较为详细的回顾和介绍,简述了对流允许尺度区域气候模式中比较重要的物理过程及外部驱动条件的影响,总结了以往对流允许尺度区域气候模拟的研究成果以及当下所面临的挑战和对未来的展望,以期对中国及东亚区域对流允许区域气候模拟的研究提供有益参考。诸多研究表明,对流允许区域气候模拟作为一种有前景的气候模式,可提供更加可靠的区域尺度的气候信息。     

Understanding the sources of Caribbean precipitation biases in CMIP3 and CMIP5 simulations

We assess the ability of Global Climate Models participating in phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5) to simulate observed annual precipitation cycles over the Caribbean. Compared to weather station records and gridded observations, we find that both CMIP3 and CMIP5 models can be grouped into three categories: (1) models that correctly simulate a bimodal distribution with two rainfall maxima in May–June and September–October, punctuated by a mid-summer drought (MSD) in July–August; (2) models that reproduce the MSD and the second precipitation maxima only; and (3) models that simulate only one precipitation maxima, beginning in early summer. These categories appear related to model simulation of the North Atlantic Subtropical High (NASH) and sea surface temperature (SST) in the Caribbean Sea and Gulf of Mexico. Specifically, models in category 2 tend to anticipate the westward expansion of the NASH into the Caribbean in early summer. Early onset of NASH results in strong moisture divergence and MSD-like conditions at the time of the May–June observed precipitation maxima. Models in category 3 tend to have cooler SST across the region, particularly over the central Caribbean and the Gulf of Mexico, as well as a weaker Caribbean low-level jet accompanying a weaker NASH. In these models, observed June-like patterns of moisture convergence in the central Caribbean and the Central America and divergence in the east Caribbean and the Gulf of Mexico persist through September. This analysis suggests systematic biases in model structure may be responsible for biases in observed precipitation variability over the Caribbean and more confidence may be placed in the precipitation simulated by the GCMs that are able to correctly simulate seasonal cycles of SST and NASH.     

不同区域气候模式对中国地区温度和降水的长期模拟比较

利用亚洲区域模式比较计划RMIP第二阶段五个区域模式和一个变网格全球模式，对中国地区1988年12月～1998年11月十年模拟的平均温度和降水结果，分析比较了不同区域气候模式对中国地区温度和降水的模拟能力。研究结果表明:几乎所有模式都能模拟出中国地区多年平均温度和降水的基本空间分布形态，但模式模拟的温度普遍偏低，在大部分区域，大多数模式模拟的降水偏多，而且不同模式之间存在较大差别。模式能较好地反映出中国地区温度的年际变化，对夏季降水的年际变化模拟较差，对冬季模拟较好。     

Interannual variability and expected regional climate change over North America

This study aims to analyse the interannual variability simulated by several regional climate models (RCMs), and its potential for disguising the effect of seasonal temperature increases due to greenhouse gases. In order to accomplish this, we used an ensemble of regional climate change projections over North America belonging to the North American Regional Climate Change Program, with an additional pair of 140-year continuous runs from the Canadian RCM. We find that RCM-simulated interannual variability shows important departures from observed one in some cases, and also from the driving models’ variability, while the expected climate change signal coincides with estimations presented in previous studies. The continuous runs from the Canadian RCM were used to illustrate the effect of interannual variability in trend estimation for horizons of a decade or more. As expected, it can contribute to the existence of transitory cooling trends over a few decades, embedded within the expected long-term warming trends. A new index related to signal-to-noise ratio was developed to evaluate the expected number of years it takes for the warming trend to emerge from interannual variability. Our results suggest that detection of the climate change signal is expected to occur earlier in summer than in winter almost everywhere, despite the fact that winter temperature generally has a much stronger climate change signal. In particular, we find that the province of Quebec and northwestern Mexico may possibly feel climate change in winter earlier than elsewhere in North America. Finally, we show that the spatial and temporal scales of interest are fundamental for our capacity of discriminating climate change from interannual variability.     

Does CMIP6 Inspire More Confidence in Simulating Climate Extremes over China?

Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during1961–2005, the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project(CMIP6),and 30 models from phase 5 of CMIP(CMIP5), are assessed in terms of spatial distribution and interannual variability. The CMIP6 multi-model ensemble mean(CMIP6-MME) can simulate well the spatial pattern of annual mean temperature,maximum daily maximum temperature, and minimum daily minimum temperature. However, CMIP6-MME has difficulties in reproducing cold nights and warm days, and has large cold biases over the Tibetan Plateau. Its performance in simulating extreme precipitation indices is generally lower than in simulating temperature indices. Compared to CMIP5, CMIP6 models show improvements in the simulation of climate indices over China. This is particularly true for precipitation indices for both the climatological pattern and the interannual variation, except for the consecutive dry days. The arealmean bias for total precipitation has been reduced from 127%(CMIP5-MME) to 79%(CMIP6-MME). The most striking feature is that the dry biases in southern China, very persistent and general in CMIP5-MME, are largely reduced in CMIP6-MME. Stronger ascent together with more abundant moisture can explain this reduction in dry biases. Wet biases for total precipitation, heavy precipitation, and precipitation intensity in the eastern Tibetan Plateau are still present in CMIP6-MME, but smaller, compared to CMIP5-MME.     

An inter-comparison of regional climate models for Europe: model performance in present-day climate

The analysis of possible regional climate changes over Europe as simulated by 10 regional climate models within the context of PRUDENCE requires a careful investigation of possible systematic biases in the models. The purpose of this paper is to identify how the main model systematic biases vary across the different models. Two fundamental aspects of model validation are addressed here: the ability to simulate (1) the long-term (30 or 40 years) mean climate and (2) the inter-annual variability. The analysis concentrates on near-surface air temperature and precipitation over land and focuses mainly on winter and summer. In general, there is a warm bias with respect to the CRU data set in these extreme seasons and a tendency to cold biases in the transition seasons. In winter the typical spread (standard deviation) between the models is 1 K. During summer there is generally a better agreement between observed and simulated values of inter-annual variability although there is a relatively clear signal that the modeled temperature variability is larger than suggested by observations, while precipitation variability is closer to observations. The areas with warm (cold) bias in winter generally exhibit wet (dry) biases, whereas the relationship is the reverse during summer (though much less clear, coupling warm (cold) biases with dry (wet) ones). When comparing the RCMs with their driving GCM, they generally reproduce the large-scale circulation of the GCM though in some cases there are substantial differences between regional biases in surface temperature and precipitation.     

Convective and stratiform precipitation characteristics in an ensemble of regional climate model simulations

We apply a recently proposed algorithm for disaggregating observed precipitation data into predominantly convective and stratiform, and evaluate biases in characteristics of parameterized convective (subgrid) and stratiform (large-scale) precipitation in an ensemble of 11 RCM simulations for recent climate in Central Europe. All RCMs have a resolution of 25 km and are driven by the ERA-40 reanalysis. We focus on mean annual cycle, proportion of convective precipitation, dependence on altitude, and extremes. The results show that characteristics of total precipitation are often better simulated than are those of convective and stratiform precipitation evaluated separately. While annual cycles of convective and stratiform precipitation are reproduced reasonably well in most RCMs, some of them consistently and substantially overestimate or underestimate the proportion of convective precipitation throughout the year. Intensity of convective precipitation is underestimated in all RCMs. Dependence on altitude is also simulated better for stratiform and total precipitation than for convective precipitation, for which several RCMs produce unrealistic slopes. Extremes are underestimated for convective precipitation while they tend to be slightly overestimated for stratiform precipitation, thus resulting in a relatively good reproduction of extremes in total precipitation amounts. The results suggest that the examined ensemble of RCMs suffers from substantial deficiencies in reproducing precipitation processes and support previous findings that climate models’ errors in precipitation characteristics are mainly related to deficiencies in the representation of convection.     

Global high resolution versus Limited Area Model climate change projections over Europe: quantifying confidence level from PRUDENCE results

Four high resolution atmospheric general circulation models (GCMs) have been integrated with the standard forcings of the PRUDENCE experiment: IPCC-SRES A2 radiative forcing and Hadley Centre sea surface temperature and sea-ice extent. The response over Europe, calculated as the difference between the 2071–2100 and the 1961–1990 means is compared with the same diagnostic obtained with nine Regional Climate Models (RCM) all driven by the Hadley Centre atmospheric GCM. The seasonal mean response for 2m temperature and precipitation is investigated. For temperature, GCMs and RCMs behave similarly, except that GCMs exhibit a larger spread. However, during summer, the spread of the RCMs—in particular in terms of precipitation—is larger than that of the GCMs. This indicates that the European summer climate is strongly controlled by parameterized physics and/or high-resolution processes. The temperature response is larger than the systematic error. The situation is different for precipitation. The model bias is twice as large as the climate response. The confidence in PRUDENCE results comes from the fact that the models have a similar response to the IPCC-SRES A2 forcing, whereas their systematic errors are more spread. In addition, GCM precipitation response is slightly but significantly different from that of the RCMs.     

Multiyear simulation of the African climate using a regional climate model (RegCM3) with the high resolution ERA-interim reanalysis

This study examines the ability of the latest version of the International Centre for Theoretical Physics (ICTP) regional climate model (RegCM3) to reproduce seasonal mean climatologies, annual cycle and interannual variability over the entire African continent and different climate subregions. The new European Center for Medium Range Weather Forecast (ECMWF) ERA-interim reanalysis is used to provide initial and lateral boundary conditions for the RegCM3 simulation. Seasonal mean values of zonal wind profile, temperature, precipitation and associated low level circulations are shown to be realistically simulated, although the regional model still shows some deficiencies. The West Africa monsoon flow is somewhat overestimated and the Africa Easterly Jet (AEJ) core intensity is underestimated. Despite these biases, there is a marked improvement in these simulated model variables compared to previous applications of this model over Africa. The mean annual cycle of precipitation, including single and multiple rainy seasons, is well captured over most African subregions, in some cases even improving the quality of the ERA-interim reanalysis. Similarly, the observed precipitation interannual variability is well reproduced by the regional model over most regions, mostly following, and sometimes improving, the quality of the ERA-interim reanalysis. It is assessed that the performance of this model over the entire African domain is of sufficient quality for application to the study of climate change and climate variability over the African continent.