邻域空间检验技术在集合降水预报检验中的应用

常规降水检验受空间及时间微小差异所带来的"双重惩罚"影响严重,邻域空间检验FSS(Fraction Skill Score)方法在确定性预报中已体现出弥补这一不足的明显优势。随着集合预报分辨率的不断提高,集合降水预报同样存在与确定性预报相似的问题。本研究将FSS方法拓展至集合预报领域,构建适用于集合预报的降水空间检验指标EFSS(Ensemble Fraction Skill Score),利用欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts, ECMWF)集合预报模式2018年夏季降水预报产品及国家气象信息中心提供的格点化降水融合产品进行分析,发现EFSS评分不受集合成员数影响,可获取一致性的评估结论。通过与适用于集合预报的常规技巧评分EETS(Ensemble Equitable Threat Score)对比分析发现,常规技巧评分受限于评分过低而无法有效反映强降水过程间差异性特征,EFSS方法则可有效提升强降水预报检验辨识度。     

Assessing Hourly Precipitation Forecast Skill with the Fractions Skill Score

Statistical methods for category (yes/no) forecasts, such as the Threat Score, are typically used in the verification of precipitation forecasts. However, these standard methods are affected by the so-called “double-penalty” problem caused by slight displacements in either space or time with respect to the observations. Spatial techniques have recently been developed to help solve this problem. The fractions skill score (FSS), a neighborhood spatial verification method, directly compares the fractional coverage of events in windows surrounding the observations and forecasts. We applied the FSS to hourly precipitation verification by taking hourly forecast products from the GRAPES (Global/Regional Assimilation Prediction System) regional model and quantitative precipitation estimation products from the National Meteorological Information Center of China during July and August 2016, and investigated the difference between these results and those obtained with the traditional category score. We found that the model spin-up period affected the assessment of stability. Systematic errors had an insignificant role in the fraction Brier score and could be ignored. The dispersion of observations followed a diurnal cycle and the standard deviation of the forecast had a similar pattern to the reference maximum of the fraction Brier score. The coefficient of the forecasts and the observations is similar to the FSS; that is, the FSS may be a useful index that can be used to indicate correlation. Compared with the traditional skill score, the FSS has obvious advantages in distinguishing differences in precipitation time series, especially in the assessment of heavy rainfall.     

A CRPS-Based Spatial Technique for the Verification of Ensemble Precipitation Forecasts

Traditional precipitation skill scores are affected by the well-known“double penalty”problem caused by the slight spatial or temporal mismatches between forecasts and observations. The fuzzy (neighborhood) method has been proposed for deterministic simulations and shown some ability to solve this problem. The increasing resolution of ensemble forecasts of precipitation means that they now have similar problems as deterministic forecasts. We developed an ensemble precipitation verification skill score, i.e., the Spatial Continuous Ranked Probability Score (SCRPS), and used it to extend spatial verification from deterministic into ensemble forecasts. The SCRPS is a spatial technique based on the Continuous Ranked Probability Score (CRPS) and the fuzzy method. A fast binomial random variation generator was used to obtain random indexes based on the climatological mean observed frequency, which were then used in the reference score to calculate the skill score of the SCRPS. The verification results obtained using daily forecast products from the ECMWF ensemble forecasts and quantitative precipitation estimation products from the OPERA datasets during June-August 2018 shows that the spatial score is not affected by the number of ensemble forecast members and that a consistent assessment can be obtained. The score can reflect the performance of ensemble forecasts in modeling precipitation and thus can be widely used.     

基于时空不确定性的对流尺度集合预报效果评估检验

针对对流尺度天气系统的高度非线性特征和高分辨率模式预报结果存在时、空不确定性现象,以及当前邻域概率法主要考虑高分辨率预报结果的空间位移误差,而不能有效解决预报结果存在时间超前与滞后问题,将时间因素引入到邻域概率法中,结合一次强飑线过程进行对流尺度集合预报试验,并基于改进后的新型邻域概率法与分数技巧评分,对降水预报进行了不同时、空尺度的效果评估检验。结果表明:（1）邻域集合概率法和概率匹配平均法在极端降水的分数技巧评分远高于传统集合平均,弥补了集合平均对极端降水预报能力偏低的缺陷。（2）对于此类飑线过程的对流尺度天气系统而言,邻域半径为15—45 km的空间尺度能够改善降水位移误差的空间不确定性,并使其预报效果达到最优,其中15—30 km的邻域半径对于尺度更小的大量级降水事件预报能力更强。（3）对流尺度降水预报考虑时间尺度与降水强度存在着对应关系,不同时间尺度可以捕获到不同量级降水的时间不确定性。同时,时间尺度与空间尺度对于降水预报效果的影响是相互关联的。（4）改进的邻域概率法能够同时体现高分辨率模式预报结果在对流尺度降水事件上存在的时、空不确定性,实现了对流尺度降水在时、空尺度上的综合评估,并能为不同量级降水提供与其时、空尺度相匹配的概率预报结果。      

三种高分辨率格点降水预报检验方法的对比

客观有效的评估高分辨率模式格点降水的预报能力,不仅是模式发展中的基础问题,而且直接关系到目前中国气象局主推的格点天气预报业务。以ECMWF(European Centre for Medium-Range Weather Forecasts)模式高分辨率降水格点预报资料、CMORPH(NOAA Climate Prediction Center Morphing Method)卫星与全国3×104个自动观测站的逐时降水量融合资料为基础,选择2015年6~8月55个降水个例,研究传统检验方法、面向对象MODE(Method for Object-based Diagnostic Evaluation)方法、以及邻域法在高分辨率格点降水预报检验中的适用性及优缺点,以期为高分辨率格点降水的预报性能评估提供参考。主要结论如下:(1)尽管点对点的传统方法在高分辨率格点降水检验中存在一定的局限,但传统方法能够在空间上表现高分辨率格点降水预报技巧的地域性差异,在时间上刻画预报的整体性能,对高分辨率格点预报性能评估仍然具有重要的适用价值;(2)邻域法的显著优点在于一方面能够通过变换邻域窗获得不同空间尺度上的传统预报技巧,另一方面独有的FSS(Fractions Skill Score)技巧评分能够表现预报相对于观测降水在格点数量上的比值,结合FSS和不同邻域窗上的传统技巧评分,可以判别在多大空间尺度上能够获得较好的预报技巧;(3)MODE方法在变换卷积半径的基础上提取降水对象,基于降水对象不仅能统计模式的传统技巧评分和预报性能的尺度变化,还可以表现降水对象的质心距离、轴角、面积、强度、综合收益、位移距离等多种属性,这些属性首先为用户提供了模式预报性能的多视角表现,其次从侧面定量描述了模式对天气系统发展快慢、槽脊强弱等预报误差,具有独特的优势,但如何应用对象属性来提高实际的预报能力还存在一些困难。     

基于FSS的高分辨率模式华北对流预报能力评估

目前高分辨率数值预报模式已具有一定的对流系统结构和演变特征预报能力,但对其预报能力的客观评估仍存在较多不足。选取2017年7—9月华北地区在不同天气系统背景下、具有不同组织模态的7次对流天气个例,使用模糊检验方法中的分数技巧评分（fraction skill score,简称FSS）指标评估不同高分辨率模式（包括快速更新同化GRAPES_Meso,GRAPES_3 km及华东区域中尺度模式）对中小尺度对流过程的预报能力。结果表明:分数技巧评分能够实现当模式预报存在位移和强度偏差时仍然给出有价值的评分结果,其优势还在于可以给出表征模式空间位移偏差尺度的预报技巧尺度信息;所用3个模式的雷达回波强度预报均偏弱,当回波强度小于44 dBZ时,华东区域中尺度模式预报最接近实况,而对于44 dBZ以上的较强回波,GRAPES_3 km模式预报偏差最小;采用百分位阈值（通过升序排列求出预报和实况数列的相同百分位数作为其相应的阈值）进行检验发现,对于预报难度更大的高阈值、小尺度的对流事件,GRAPES_3 km模式预报能力更强。     

尺度分解技术在定量降水临近预报检验中的应用

采用2004年Casati提出的强度-尺度检验技术,选取2008年汛期代表不同类型降水(对流云降水、层状云降水、混合云降水)的4个降水过程,从尺度分解角度入手,对"世界气象组织天气研究计划——北京奥运会预报示范项目"(WWRP B08FDP)项目中4个I临近预报参加系统(BJANC,GRAPES-SWIFT,STEPS,CARDS)的1h定量降水预报进行时空尺度分解检验,研究降水预报技巧与降水时空尺度和强度之间的关系。结果表明:尽管目前国际先进的临近预报系统的水平分辨率已高达1～2km,但其有技巧的临近预报能力主要集中于空间尺度大于32km、时间尺度大于1h的降水系统,而对小于这些尺度的降水系统预报能力仍非常有限;在不同时空尺度的临近预报降水误差中,60%以上的误差来自于空间尺度小于8km的降水,85%以上的误差来自于时间尺度小于1h的降水,传统的外推技术不能满足这些较小时空尺度降水预报的需求,要发展有效的预报方法来提高较小时空尺度降水的预报能力。将基于外推的临近预报和基于稠密观测资料、快速更新的数值预报的潜势预报相结合可能是一条有效的解决途径。     

一种集成风向风速的风场空间检验方法

基于概率分布特征定义全新风速阈值选取方案,不受地域及季节性影响,并综合风向信息建立兼顾风向风速的风场分类列表,采用邻域空间检验技术构建可集成风向风速的矢量风场检验方法。基于2018年4月1—30日GRAPES_Meso模式不同分辨率（10 km及3 km）逐小时预报产品,利用所开发的矢量风场检验方法分析表明:模式风向预报的随机性随着风速的增大而减小,即弱风的风向难以成功预报。通过矢量风场综合分析发现高分辨率预报效果在170 km空间尺度上24 h预报最大评分优势可达0.24,各邻域空间尺度上评分分布趋势保持一致。通过敏感性分析发现,所获取的综合指标可用于反映风场预报性能。同时,不同矢量风场分类方法将对评估结果产生影响,高分类方法评分稳定性更好,低分类方法受限于单一分类权重过大而影响评估一致性。因此,在计算能力允许的条件下,选择较高分类方式将有助于获得更为稳定的检验效果。     

智能网格SCMOC及多模式降水预报对比

以三源融合网格实况降水分析资料CMPAS为参照,基于二分法经典检验、预报评分综合图和面向对象MODE检验等方法,对比分析2021年智能网格预报SCMOC以及ECMWF全球、CMA-Meso中尺度模式在秦岭及周边地区的降水预报表现,主要结论如下:1)ECMWF能够很好地刻画日平均降水量、日降水量标准差以及地形影响下降水量、降水频次的空间分布特征,但对于0.1 mm以上量级的降水预报频次远高于观测,暴雨预报频次低于观测,SCMOC、CMA-Meso日降水量大于等于0.1 mm的降水频次和暴雨频次预报更好;SCMOC不足在于降水的空间精细分布特征描述能力相对较弱。2)ECMWF预报的大于等于0.1 mm降水频次日峰值出现时间整体较观测偏早3 h左右,CMA-Meso、SCMOC与观测总体吻合较好。3)三种产品24 h降水量大于等于0.1 mm的TS(Threat Score)评分数值上基本一致,但降水预报表现的特征显著不同,SCMOC成功率高、命中率低,漏报多、空报少,ECMWF、CMA-Meso则相反;24 h、3 h大雨以上量级降水SCMOC的TS评分、成功率、命中率一致优于其他两种产品。4)MODE暴雨检验,SCMOC大面积降水对象与观测相似度最高,预报能力优于ECMWF,但分散性小面积暴雨对象漏报风险大。SCMOC、ECMWF纬向距离偏差大于经向,位置偏西比例高于偏东。     

A convection-allowing ensemble forecast based on the breeding growth mode and associated optimization of precipitation forecast

A convection-allowing ensemble forecast experiment on a squall line was conducted based on the breeding growth mode (BGM). Meanwhile, the probability matched mean (PMM) and neighborhood ensemble probability (NEP) methods were used to optimize the associated precipitation forecast. The ensemble forecast predicted the precipitation tendency accurately, which was closer to the observation than in the control forecast. For heavy rainfall, the precipitation center produced by the ensemble forecast was also better. The Fractions Skill Score (FSS) results indicated that the ensemble mean was skillful in light rainfall, while the PMM produced better probability distribution of precipitation for heavy rainfall. Preliminary results demonstrated that convection-allowing ensemble forecast could improve precipitation forecast skill through providing valuable probability forecasts. It is necessary to employ new methods, such as the PMM and NEP, to generate precipitation probability forecasts. Nonetheless, the lack of spread and the overprediction of precipitation by the ensemble members are still problems that need to be solved.     

Percentile-based neighborhood precipitation verification and its application to a landfalling tropical storm case with radar data assimilation

The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias.In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias.When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification.     

2006年汛期北京地区中尺度数值业务降水预报检验

采用北京地区自动站逐小时观测降水资料对2006年汛期北京地区中尺度数值业务降水预报效果进行了客观检验,并针对2006年汛期的降水特点对模式的降水预报性能进行了初步的评估,着重对发生的28次降水过程按其主导的天气系统进行了分类,并对各个类型的降水预报进行了评分检验,根据检验结果分析了数值业务模式对于夏季不同天气系统导致的降水过程的预报能力,并且对不同分辨率的模式网格的预报性能进行了初步对比。     

一种基于可预报性的暴雨预报评分新方法Ⅱ:暴雨检验评分模型及评估试验

针对当前暴雨预报检验采用二分类事件检验方法存在较严重的“空报”“漏报”双重惩罚,没有考虑暴雨时空分布不均和预报评分可比性不够等问题,在分析预报员对暴雨预报评分期望值基础上,设计了一种基于可预报性的暴雨预报检验评分新方法和计算模型,分析了理想评分,并对2015-2016年4-10月中国中央气象台5 km×5 km定量降水格点预报和降水落区等级暴雨预报进行评分试验,获得了以下结果和结论:（1）预报员对暴雨预报评分期望值呈现梯级下降特征,与传统的TS评分存在显著差异;（2）设计了一种基于可预报性的暴雨预报检验新方法,通过引入e指数函数构建暴雨预报评分基函数,进而构建暴雨评分模型,该模型可以较好地拟合预报员对暴雨预报评分的期望值,同时改善了评分在不同量级阈值处的断崖式突变情况;（3）提出了预报与观测的邻域匹配方法,即一个预报点与所定义邻域中的一组观测相匹配,并利用距离加权最大值法确定暴雨评分值权重系数,预报与观测距离越近,距离权重系数越大,评分值权重越大,提高了评分的合理性,避免了距离较远的匹配站点得高分不利于鼓励预报员提高预报精度的问题;（4）对中国中央气象台逐日5 km×5 km水平分辨率的定量降水格点预报产品和中央气象台定量降水落区等级预报产品进行了评分试验,暴雨预报准确率全国平均值大于60分。基于可预报性的暴雨预报检验新评分与传统暴雨预报TS评分逐日演变特征相似,但可以较好地解析TS为0的预报评分,解析后的新评分与预报员和公众的心理预期更为接近。      

基于因子分析的广东省短时强降水预报模型及其业务试验

利用每天4次0.125°×0.125°的ECMWF-Interim再分析资料和广东省2009—2018年地面气象站逐时雨量观测的短时强降水数据集,针对广东不同季节、不同地域的短时强降水,以提高命中率同时控制虚警率为目的,提出基于显著性和敏感性评价的物理量优选和因子分析法,用于构建分期、分区的广东短时强降水概率预报模型。以参数显著性和预测敏感性为标准,在49个待选物理量中挑选18个既与多年平均态存在明显差异,又具有较低虚警率的物理量,应用方差最大正交旋转因子分析法将遴选物理量组合成表征大气不同环境条件的6个因子;为使组合因子更具适应性,基于因子偏离度特征对广东前、后汛期不同区域独立建模,构建分期、分区短时强降水逐6 h格点概率预报模型。汛期业务试验表明,模型对短时强降水发生概率预报效果较好。对2019年汛期模型每天两次起报的12 h预报时效内概率产品进行格点检验,以训练期最优TS评分对应的固定概率作为预测概率阈值,广东省大部分区域TS评分超过0.25,最高超过0.42,平均较ECMWF-Fine业务模式在前、后汛期分别提升0.23与0.21,南部沿海TS评分提升幅度最大,并且模型在提升命中率与降低虚警率之间取得较好的平衡。个例分析表明,对于ECMWF模式常漏报的广东暖区短时强降水,概率预报模型具有明显优势,尤其能为天气尺度弱动力强迫的强降水早期预警提供更多有效信息。      

Guidance on the Choice of Threshold for Binary Forecast Modeling

This paper proposes useful guidance on the choice of threshold for binary forecasts. In weather forecast systems, the probabilistic forecast cannot be used directly when estimated too smoothly. In this case, the binary forecast, whether a meteorological event will occur or not, is preferable to the probabilistic forecast. A threshold is needed to generate a binary forecast, and the guidance in this paper encompasses the use of skill scores for the choice of threshold according to the forecast pattern. The forecast pattern consists of distribution modes of estimated probabilities, occurrence rates of observations, and variation modes. This study is performed via Monte-Carlo simulation, with 48 forecast patterns considered. Estimated probabilities are generated by random variate sampling from five distributions separately. Varying the threshold from 0 to 1, binary forecasts are generated by threshold. For the assessment of binary forecast models, a 2×2 contingency table is used and four skill scores （Heidke skill score, hit rate, true skill statistic, and threat score） are compared for each forecast pattern. As a result, guidance on the choice of skill score to find the optimal threshold is proposed.     

华南区域高分辨率数值模式前汛期预报初步评估

基于华南地区自动站逐小时观测资料, 采用传统站点评分、邻域法等评估华南区域高分辨率数值模式(包括GRAPES_GZ_R 1 km模式和GRAPES_GZ 3 km模式)对降水、地面温度和风场等要素的预报能力。结果表明: GRAPES_GZ_R 1 km模式的降水预报技巧优于GRAPES_GZ 3 km模式, 模式预报以正偏差为主。对于不同起报时间的预报, 00时(世界时, 下同)起报的预报效果优于12时。GRAPES_GZ_R 1 km模式的TS评分是GRAPES_GZ 3 km模式的两倍以上, 对不同降水阈值的评分均较高。分数技巧评分(FSS)显示GRAPES_GZ_R 1 km模式6 h累计降水预报在0.1 mm、1 mm及5 mm以上的降水均可达到最低预报技巧尺度, 对所检验降水对象的空间位置把握能力更好。2 m气温和10 m风速检验结果表明两个模式均能较好把握广东省温度的分布特征, GRAPES_GZ_R 1 km模式对2 m气温预报结果优于GRAPES_GZ 3 km模式, 预报绝对误差更小; 两个模式对风速的预报整体偏强, 预报偏差在1~4 m/s之间, 但相比之下GRAPES_GZ 3 km模式在风场预报上表现更好。GRAPES_GZ_R 1 km模式的2 m气温和10 m风速预报偏差随降水过程存在明显波动, 强降水过后温度预报整体偏低, 风速预报偏强, 在模式产品订正、使用等需要考虑模式对主要天气系统的预报情况。总的来说, GRAPES_GZ_R 1 km模式的预报产品具有较好的参考价值。      

2013年汛期华中区域业务数值模式降水预报检验

为充分了解华中区域中尺度业务数值预报模式更新为WRF后的预报性能,对该模式2013年汛期24 h和48 h的累积降水预报产品,采用TS评分、预报正确率、漏报率、空报率、偏差及ETS评分等统计量对其进行了较详细的评估。结果表明：从日平均降水率分布来看,24 h预报的降水中心位置和强度与实况更接近,48 h的预报明显偏大、偏强;汛期总体降水检验表明,该模式的降水预报以偏大为主,随着降水量级的增大,TS和ETS评分逐渐减小,且ETS评分逐渐靠近TS;逐月降水检验结果发现,该区域汛期月晴雨预报正确率与雨日率呈正相关;通过梅雨期WRF与GRAPES_Meso的预报对比检验可见,两个模式都表现出了较好的预报性能。值得指出的是,随着降水量级的增大,WRF模式降水预报优势逐渐显现。总的来说,该模式的降水预报产品具有一定的参考价值。     

蒙特卡罗方法在降水评估中的应用研究

预报检验重点关注预报与观测间的综合统计特征用以探讨模式预报性能,而统计显著性检验方法是衡量评估结论的重要指标,是判断预报效果改进与否的有效手段.当前诸多重要检验指标如降水技巧评分等由于不满足正态分布特征均难以采用简单的计算方式获得置信区间以衡量检验指标的误差特征,因此难以正确判断通过统计检验所获得的评估差异是真实反映模式预报效果差异或是由检验样本的不确定性所造成.蒙特卡罗方法可通过样本重构获取正态分布的统计样本从而有效地解决这一问题.采用2015年8月的T639模式及GRAPES全球预报模式24 h降水预报产品,使用中国区域2400站日降水资料作为实况,重点研究蒙特卡罗方法在统计显著性检验中的应用特征,分析不同蒙特卡罗重构次数对检验结果的收敛性.结果表明10 000次蒙特卡罗重构后统计指标可满足正态分布,而通过显著性检验分析后可明显区分预报系统间降水评分差异的统计特征.     

Multimodel based superensemble forecasts for short and medium range NWP over various regions of Africa

Summary This study examines the predictability of weather over several regions in Africa using a multimodel superensemble technique developed at the Florida State University, which is an objective means of combining daily forecasts from multilevel global models. It is referred to as FSUSE and up to 7 different models are used to construct the superensemble. The benchmark reanalysis fields used are the precipitation data sets from CMORPH and all other global fields from ECMWF daily operational analysis. The FSUSE works by using multiple linear regression to derive weights from a comparison of each member model forecast to the benchmark analysis during a training period of the most recent 120 days, and these weights are passed to the forecast phase. This procedure removes the bias of each model and allows for an optimal linear combination of the individual model forecasts by taking account of the relative skill of each model to give a consensus forecast that is superior to the ensemble mean and all the members. Results show that bad models and poor analysis fields used during the training phase degrade the skill of the FSUSE. In the forecasts of rainfall events over all regions of Africa, the FSUSE root-mean-square (R M S) error, equitable threat skill score (E T S), and bias on the daily forecasts of rainfall were invariably superior to the best member model. The skills deteriorate as the forecast lead time in days increases, with the degradation being most significant beyond day 3. In all cases, the bias score of the FSUSE was approximately 1, while the anomaly correlation scores were to the order of 0.9. These scores indicate the robustness of the FSUSE forecasts. Over East Africa, the FSUSE forecasts were consistent with the spatial-temporal pattern of the Intertropical Convergence Zone (ITCZ), the main rain bearing synoptic mechanism across tropical Africa. Thus, in addition to superior forecasts, the use of FSUSE based data sets may provide a better understanding of the dynamical processes within the ITCZ over the region. These results could be further improved if the daily series of operational analysis had included gauge data and if the resolution were higher. It is hardly possible to get uniformly consistent and continuous daily observations over these diverse regions of Africa. However, given the availability of the satellite based estimates of daily rainfall, such as CMORPH and global analysis that are exchanged very fast nowadays, the FSUSE scheme for numerical weather predictions (N W P) provides useful medium range weather forecasts in real-time.     

An Experiment of a Statistical Downscaling Forecast Model for Summer Precipitation over China

A combination of the optimal subset regression (OSR) approach, the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to construct a statistical downscaling forecast model for precipitation in summer. Retroactive forecasts are performed to assess the skill of statistical downscaling during the period from 2003 to 2009. The results show a poor simulation for summer precipitation by the NCC- CGCM for China, and the average spatial anomaly correlation coefficient (ACC) is 0.01 in the forecast period. The forecast skill can be improved by OSR statistical downscaling, and the OSR forecast performs better than the NCC-CGCM in most years except 2003. The spatial ACC is more than 0.2 in the years 2008 and 2009, which proves to be relatively skillful. Moreover, the statistical downscaling forecast performs relatively well for the main rain belt of the summer precipitation in some years, including 2005, 2006, 2008, and 2009. However, the forecast skill of statistical downscaling is restricted to some extent by the relatively low skill of the NCC- CGCM.