Improving the seasonal forecast for summertime South China rainfall using statistical downscaling

Similar documents
Statistical Downscaling of Precipitation in Korea Using Multimodel Output Variables as Predictors

Statistical Downscaling of Pattern Projection Using Multi-Model Output Variables as Predictors

The Interdecadal Variation of the Western Pacific Subtropical High as Measured by 500 hpa Eddy Geopotential Height

The Coupled Model Predictability of the Western North Pacific Summer Monsoon with Different Leading Times

Oceanic origin of the interannual and interdecadal variability of the summertime western Pacific subtropical high

Decrease of light rain events in summer associated with a warming environment in China during

Six month lead downscaling prediction of winter to spring drought in South Korea based on a multimodel ensemble

Weakening relationship between East Asian winter monsoon and ENSO after mid-1970s

Toward enhancement of prediction skills of multimodel ensemble seasonal prediction: A climate filter concept

The increase of snowfall in Northeast China after the mid 1980s

Climate Outlook for March August 2018

Climate Outlook for March August 2017

The Formation of Precipitation Anomaly Patterns during the Developing and Decaying Phases of ENSO

Reprint 675. Variations of Tropical Cyclone Activity in the South China Sea. Y.K. Leung, M.C. Wu & W.L. Chang

Climate Outlook for October 2017 March 2018

Climate Outlook for Pacific Islands for May - October 2015

Monsoon Activities in China Tianjun ZHOU

Seasonal Prediction of Summer Temperature over Northeast China Using a Year-to-Year Incremental Approach

Statistical downscaling methods based on APCC multi-model ensemble for seasonal prediction over South Korea

Impact of overestimated ENSO variability in the relationship between ENSO and East Asian summer rainfall

East-west SST contrast over the tropical oceans and the post El Niño western North Pacific summer monsoon

Transition of the annual cycle of precipitation from double-peak mode to single-peak mode in South China

Evaluation of the Twentieth Century Reanalysis Dataset in Describing East Asian Winter Monsoon Variability

Forced and internal variability of tropical cyclone track density in the western North Pacific

Development of Multi-model Ensemble technique and its application Daisuke Nohara

Climate Outlook for Pacific Islands for December 2017 May 2018

Sensitivity of summer precipitation to tropical sea surface temperatures over East Asia in the GRIMs GMP

Climate Outlook for Pacific Islands for July December 2017

Climate Outlook for December 2015 May 2016

IAP Dynamical Seasonal Prediction System and its applications

Climate Outlook for Pacific Islands for August 2015 January 2016

Interdecadal and Interannnual Variabilities of the Antarctic Oscillation Simulated by CAM3

Respective impacts of the East Asian winter monsoon and ENSO on winter rainfall in China

South Asian Climate Outlook Forum (SASCOF-6)

Development of a statistical downscaling model for projecting monthly rainfall over East Asia from a general circulation model output

East China Summer Rainfall during ENSO Decaying Years Simulated by a Regional Climate Model

P2.11 DOES THE ANTARCTIC OSCILLATION MODULATE TROPICAL CYCLONE ACTIVITY IN THE NORTHWESTERN PACIFIC

Why do dust storms decrease in northern China concurrently with the recent global warming?

Large-scale atmospheric singularities and summer long-cycle droughts-floods abrupt alternation in the middle and lower reaches of the Yangtze River

Skills of yearly prediction of the early-season rainfall over southern China by the NCEP climate forecast system

High initial time sensitivity of medium range forecasting observed for a stratospheric sudden warming

Prediction of Tropical Cyclone Landfall Numbers Using a Regional Climate Model

The ENSO s Effect on Eastern China Rainfall in the Following Early Summer

SUPPLEMENTARY INFORMATION

The Australian Summer Monsoon

TREND AND VARIABILITY OF CHINA PRECIPITATION IN SPRING AND SUMMER: LINKAGE TO SEA-SURFACE TEMPERATURES

Instability of the East Asian Summer Monsoon-ENSO Relationship in a coupled global atmosphere-ocean GCM

Analysis Links Pacific Decadal Variability to Drought and Streamflow in United States

A Quick Report on a Dynamical Downscaling Simulation over China Using the Nested Model

Baoqiang Xiang 1, Bin Wang 1,2, Weidong Yu 3, Shibin Xu 1,4. Accepted Article

Predictability and prediction of the North Atlantic Oscillation

Reprint 527. Short range climate forecasting at the Hong Kong Observatory. and the application of APCN and other web site products

SUPPLEMENTARY INFORMATION

Interannual Relationship between the Winter Aleutian Low and Rainfall in the Following Summer in South China

APCC/CliPAS. model ensemble seasonal prediction. Kang Seoul National University

Modulation of PDO on the predictability of the interannual variability of early summer rainfall over south China

Variations of frequency of landfalling typhoons in East China,

4.3.2 Configuration. 4.3 Ensemble Prediction System Introduction

Impact of the Atlantic Multidecadal Oscillation on the Asian summer monsoon

Decadal variability of the IOD-ENSO relationship

An observational study of the impact of the North Pacific SST on the atmosphere

Projections of the 21st Century Changjiang-Huaihe River Basin Extreme Precipitation Events

Charles Jones ICESS University of California, Santa Barbara CA Outline

1. Introduction. 2. Verification of the 2010 forecasts. Research Brief 2011/ February 2011

Recent weakening of northern East Asian summer monsoon: A possible response to global warming

10.5 ATMOSPHERIC AND OCEANIC VARIABILITY ASSOCIATED WITH GROWING SEASON DROUGHTS AND PLUVIALS ON THE CANADIAN PRAIRIES

SCIENCE CHINA Earth Sciences. Design and testing of a global climate prediction system based on a coupled climate model

Long-Term Trend and Decadal Variability of Persistence of Daily 500-mb Geopotential Height Anomalies during Boreal Winter

Inactive Period of Western North Pacific Tropical Cyclone Activity in

PUBLICATIONS. Journal of Geophysical Research: Atmospheres

Introduction of climate monitoring and analysis products for one-month forecast

FUTURE PROJECTIONS OF PRECIPITATION CHARACTERISTICS IN ASIA

Impacts of Climate Change on Autumn North Atlantic Wave Climate

High-Resolution Late 21st-Century Projections of Regional Precipitation by Empirical Downscaling from Circulation Fields of the IPCC AR4 GCMs

Topic 3.2: Tropical Cyclone Variability on Seasonal Time Scales (Observations and Forecasting)

South Asian Climate Outlook Forum (SASCOF-12)

Seasonal Climate Outlook for South Asia (June to September) Issued in May 2014

Projected change in extreme rainfall events in China by the end of the 21st century using CMIP5 models

KUALA LUMPUR MONSOON ACTIVITY CENT

Anticorrelated intensity change of the quasi-biweekly and day oscillations over the South China Sea

Seasonal Climate Watch June to October 2018

JMA s Seasonal Prediction of South Asian Climate for Summer 2018

Possible impact of the autumnal North Pacific SST and November AO on the East Asian winter temperature

Delayed Response of the Extratropical Northern Atmosphere to ENSO: A Revisit *

The Spring Predictability Barrier Phenomenon of ENSO Predictions Generated with the FGOALS-g Model

Influence of South China Sea SST and the ENSO on Winter Rainfall over South China CHAN 2,3

ASIA-PACIFIC JOURNAL OF ATMOSPHERIC SCIENCES, 45, 1, 2009, p

Impact of Eurasian spring snow decrement on East Asian summer precipitation

1. Introduction. 3. Climatology of Genesis Potential Index. Figure 1: Genesis potential index climatology annual

Nonlinear atmospheric teleconnections

Experimental Forecasts of Seasonal Forecasts of Tropical Cyclone Landfall in East Asia (Updated version with Jun-Dec forecasts) 3.

CLIMATE SIMULATION AND ASSESSMENT OF PREDICTABILITY OF RAINFALL IN THE SOUTHEASTERN SOUTH AMERICA REGION USING THE CPTEC/COLA ATMOSPHERIC MODEL

Introduction of products for Climate System Monitoring

Research progress of snow cover and its influence on China climate

Different Relationships Between Spring SST in the Indian and Pacific Oceans and Summer Precipitation in China

Dynamical prediction of the East Asian winter monsoon by the NCEP Climate Forecast System

A Study of Teleconnection between the South Asian and East Asian Monsoons: Comparison of Summer Monsoon Precipitation of Nepal and South Korea

Long-term climate variations in China and global warming signals

UPDATE OF REGIONAL WEATHER AND SMOKE HAZE (September 2017)

BCC climate prediction model system: developments and applications

Transcription:

JOURNAL OF GEOPHYSICAL RESEARCH: ATMOSPHERES, VOL. 118, 5147 5159, doi:10.1002/jgrd.50367, 2013 Improving the seasonal forecast for summertime South China rainfall using statistical downscaling Ying Lut Tung, 1 Chi-Yung Tam, 1,2 Soo-Jin Sohn, 3 and Jung-Lien Chu 4 Received 6 August 2012; revised 20 March 2013; accepted 23 March 2013; published 6 June 2013. [1] The performance of various seasonal forecast systems in predicting the station-scale summer rainfall in South China (SC) was assessed and was compared with that based on a statistical downscaling scheme. Hindcast experiments from 11 dynamical models covering the period of 1983 2003 were taken from the Asia-Pacific Economic Cooperation Climate Center multimodel ensemble. Based on observations, singular value decomposition analysis (SVDA) showed that SC precipitation is strongly related to the broad-scale sea level pressure (SLP) variation over Southeast Asia, western north Pacific, and part of the Indian Ocean. Analogous covariability was also found between model hindcasts and the observed station precipitation. Based on these results from SVDA, a statistical downscaling scheme for predicting SC station rainfall with model SLP as predictor was constructed. In general, the statistical scheme is superior to the original model prediction in two geographical regions, namely, western SC (near Guangxi) and eastern coastal SC (eastern Guangdong to part of Fujian). Further analysis indicated that dynamical models are able to reproduce the large-scale circulation patterns associated with the recurrent modes of SC rainfall, but not the local circulation features. This probably leads to erroneous rainfall predictions in some locations. On the other hand, the statistical scheme was able to map the broad-scale SLP patterns onto the station-scale rainfall anomalies, thereby correcting some of the model biases. Overall, our results demonstrate how SC summer rainfall predictions can be improved by tapping the source of predictability related to large-scale circulation signals from dynamical models. Citation: Tung, Y. L., C.-Y. Tam, S.-J. Sohn, and J.-L. Chu (2013), Improving the seasonal forecast for summertime South China rainfall using statistical downscaling, J. Geophys. Res. Atmos., 118, 5147 5159, doi:10.1002/jgrd.50367. 1. Introduction [2] Floods and droughts are a cause of serious social and economic losses in China [Chen, 1991]. This is particularly true for South China (SC), which is densely populated with a fast developing economy. Previous studies indicated that the summertime SC precipitation is affected by a number of climate systems, making its prediction very challenging. Wu and Wang [2000] and Wang et al. [2001] suggested that the western north Pacific summer monsoon (WNPSM) activity affects the seasonal mean precipitation over SC. Mao et al. [2011] showed that the dominant patterns of 1 School of Energy and Environment, City University of Hong Kong, Hong Kong, China. 2 Guy Carpenter Asia-Pacific Climate Impact Centre, City University of Hong Kong, Hong Kong, China. 3 Climate Prediction Operation Team, Asia-Pacific Economic Cooperation Climate Center, Busan, South Korea. 4 National Science and Technology Center for Disaster Reduction, Taipei, Taiwan. Corresponding author: C.-Y. Tam, School of Energy and Environment, City University of Hong Kong, Tat Chee Avenue, Kowloon, Hong Kong, China. (franctam@cityu.edu.hk) 2013. American Geophysical Union. All Rights Reserved. 2169-897X/13/10.1002/jgrd.50367 interannual variation of early summer SC rainfall themselves depend on the phase of the Pacific decadal oscillation (PDO). Ashok et al. [2004], Wang and Guan [2007], and Zhao et al. [2011] investigated the impact of Indian Ocean dipole (IOD) [Saji et al., 1999] on the summer SC precipitation. Yu et al. [2001] also argued that warming in the Indian Ocean affects summer monsoon rainfall over mid-eastern China and southeastern China. Tang and Sun [2005] illustrated how IOD and the subtropical high influence the summer SC precipitation. Other climate modes or climate elements such as El Niño Southern Oscillation and the location/strength of the western Pacific subtropical ridge also have an impact on the SC circulation [Gong and Wang, 1999; Chang et al., 2000; Lu et al., 2002; Gong and Ho, 2002]. [3] Nowadays, general circulation model (GCM) based dynamical forecast systems are commonly used for seasonal prediction. Although they have the ability to reproduce large-scale circulation, GCMs show very low skill in predicting the local rainfall. This is because the latter is strongly affected by topography or land-sea contrasts which are usually not well represented in coarse-scale models [e.g., Rodwell, 1998; von Storch et al., 1993]. In order to obtain regional climate information from coarse-scale GCM products, either dynamical or statistical downscaling can be employed [von Storch et al., 1993; Wilby and Wigley, 5147

Figure 1. (a) Geographical map of China and provinces covering the study area (shading). (b) Location of selected stations (open circle). 1997]. For statistical downscaling, the empirical relation between one local climate variable (the predictand) and the other (the predictor) is first sought [Zorita and von Storch, 1999; Wilby et al., 2004]. Forecasts of the local variable are then produced by projecting predictor values from GCMs into the corresponding statistical relationship. Liu et al. [2011] used the Antarctic Oscillation Index [Thompson and Wallace, 2000], 500 hpa geopotential height, 850 hpa humidity, and sea surface temperature as predictors and found increased skill in predicting the summer precipitation in southeastern China. Other predictors such as mean sea level pressure (SLP) [Cavazos, 1999; Wetterhall et al., 2005], geopotential height [Zhu et al., 2008], relative vorticity [Wilby et al., 1998], or even precipitation itself [Widmann et al., 2003] from coarse-resolution models were used for predicting the local-scale rainfall. [4] Perfect prognosis (PP) and model output statistics (MOS) are the two commonly used statistical downscaling approaches [Wilks, 1995; see also the review by Maraun et al., 2010]. In the former approach, downscaling is constructed based on relationships between observed predictors and observed local predictands [Klein et al., 1959]. Such relationships can be found by, say, regression related methods, while the choice of predictors is often motivated by the associated atmospheric dynamics. By using modelgenerated variables as predictors in PP, one assumes that these variables are realistically simulated. The advantage of PP is that prediction equations (established from observational records) can be directly used to downscale GCM products without any modification. On the other hand, the performance of PP can be sensitive to model biases. In the MOS approach, statistical relationships between GCM products and observed predictands are established directly. By construction, therefore, statistical downscaling based on MOS can correct GCM errors. In other words, MOS combines bias correlation and downscaling in one step Table 1. Description of the Model Hindcast Experiments Used in This Study Member Economy Institute Model Resolution Ensemble Size Experimental Type Reference Australia Bureau of Meteorological Predictive Ocean-Atmosphere T47 L17 10 CMIP Zhong et al. [2005] Research Centre (BMRC) Model for Australia (POAMA) Canada Meteorological Service of MSC-GM2 T32 L10 10 SMIP/HFP McFarlane et al. [1992] Canada (MSC) Canada Meteorological Service of MSC-GM3 T63 L32 10 SMIP/HFP Scinocca et al. [2008] Canada (MSC) Canada T95 L27 10 SMIP/HFP Ritchie [1991] China South Korea South Korea South Korea South Korea Chinese Taipei United States Meteorological Service of Canada (MSC) Beijing Climate Center (BCC) Korean Meteorological Administration (KMA) National Institute of Meteorological Research (NIMR) Pusan National University (PNU) Seoul National University (SNU) Central Weather Bureau (CWB) National Centers for Environmental Prediction (NCEP) MSC Spectral Primitive Equation Model (MSC-SEF) BCC CGCM T63 L16 8 CMIP Ding et al. [2000] Global Data Assimilation and Prediction System (GDAPS) Meteorological Research Institute AGCM T106 L21 20 SMIP/HFP Park et al. [2002] 5º 4º L17 10 SMIP/HFP Back et al. [2002] PNU CGCM T42 L18 5 CMIP Sun and Ahn [2011] Global Climate Prediction T63 L21 12 SMIP/HFP Kang et al. [2004] System (GCPS) CWB AGCM T42 L18 10 SMIP/HFP Liou et al. [1997] NCEP Climate Forecast System (CFS) T62 L64 15 CMIP Saha et al. [2006] 5148

Figure 2. The leading singular vector for precipitation based on SVD analysis between observed station precipitation and model SLP from (a) BCC, (b) CWB, (c) GCPS, (d) GDAPS, (e) MSC-GM2, (f) MSC-GM3, (g) MSC-SEF, (h) NCEP, (i) NIMR, (j) PNU, (k) POAMA, and (l) the MME average. Upper right of each panel shows the fraction of squared covariance between station precipitation and model SLP explained by this SVD mode. [Maraun et al., 2010]. In this study, an MOS-based statistical downscaling scheme for predicting the SC station-scale precipitation is constructed based on singular value decomposition analysis (SVDA). Later, it will be seen that there are biases in the GCM-simulated large-scale circulation such that the use of MOS, instead of PP, is necessary for this problem. The predictor values are taken from hindcasts of models comprising the Asia-Pacific Economic Cooperation Climate Center (APCC) multimodel ensemble (MME) [Min et al., 2011; Lee et al., 2011; Sohn et al., 2011]. It is well recognized that the MME approach can lead to more accurate forecasts owing to better sampling of model uncertainties [Krishnamurti et al., 1999; Doblas-Reyes et al., 2000; Shukla et al., 2000; Palmer et al., 2000]. The skill of both the statistically downscaled prediction and the spatially interpolated direct model output (DMO), from both individual models as well as their MME average, will be analyzed. Note that our approach is different from that of Liu et al. [2011], which is based on multiple linear regression between rainfall at each station and a predetermined set of climate indices. This is similar to the 1-D maximum covariance analysis approach [Widmann, 2005], which is an alternative to pattern-based methods. However, in this study, the patternbased SVDA is preferred because of its ability to reveal any linkages between large-scale variables and local precipitation [Wilby and Wigley, 1997]. The rest of this paper is organized as follows. The description of the observational and model hindcast data sets, as well as the methodology, is given in section 2. In section 3, the relationship between SC rainfall and the large-scale circulation is presented, and the skills of seasonal prediction from dynamical models and statistical downscaling are compared. Finally, discussions and summary can be found in section 4. 2. Data and Methodology 2.1. Observations and Model Hindcast Data [5] The observational data used in this study mainly consist of station precipitation during the season of June, July, and August (JJA) for the period of 1983 2003. Observations 5149

Figure 3. Same as Figure 2 except for singular vectors for model SLP. from 740 stations in SC within the domain of 18 N 27 N, 105 E 120 E, which includes Guangdong, Hainan, Fujian, Hunan, Jiangxi, and Guizhou provinces, as well as Guangxi, Hong Kong, and Macau, were examined. After screening out stations with missing values in the observational records, 89 stations in the SC region were selected. From Figure 1b, it can be seen that they have a rather uniform spatial distribution with about one to two stations within each 1º 1º subregion. Besides station precipitation, mean sea level pressure (SLP) and 500 hpa geopotential height (Z500) from the National Centers for Environmental Prediction Department of Energy (NCEP-DOE) Atmospheric Model Intercomparison Project (AMIP-II) reanalysis [Kanamitsu et al., 2002] with 2.5º 2.5º resolution were also used. [6] The models examined are the 11 climate models participating in the APCC MME seasonal forecast. Table 1 gives a description of the hindcast experiments. The experimental types are those consistent with either the Seasonal Model Intercomparison Project/Historical Forecast Project (SMIP/HFP) or the Coupled Model Intercomparison Project (CMIP). The former type includes forecasts from the Canadian Climate Centre second-generation [McFarlane et al., 1992] and third-generation general circulation models [Scinocca et al., 2008], and also the multilevel spectral primitive equation model [Ritchie, 1991] of the Meteorological Service of Canada (MSC), the Global Data Assimilation, and Prediction System (GDAPS) of the Korea Meteorological Administration (KMA) [Park et al., 2002], the Meteorological Research Institute Atmospheric General Circulation Model [Back et al., 2002] of the National Institute of Meteorological Research (NIMR), the Global Climate Prediction System (GCPS) from the Seoul National University (SNU) [Kang et al., 2004], and the second-generation global forecast system at the Central Weather Bureau (CWB) in Taiwan [Liou et al., 1997]. The latter type of experiments is those from the Predictive Ocean-Atmosphere Model for Australia (POAMA) of the Bureau of Meteorology Research Centre (BMRC), Australia [Zhong et al., 2005], the coupled general circulation model (CGCM) of the Beijing Climate Center (BCC), China [Ding et al., 2000], the Pusan National University (PNU) CGCM, Korea [Sun and Ahn, 2011], and the NCEP Coupled Forecast System (CFS) [Saha et al., 2006]. The common model hindcast period is from 1983 to 2003. For each set of model run, historical predictions for JJA were initialized in May with slightly different initial conditions for each member in the ensemble integration. Finally, meteorological variables including SLP and Z500 from each individual models as well as their MME average (defined as the simple average of outputs from all models) were considered. All model data were interpolated on a 2.5º 2.5º regular grid. 2.2. Statistical Downscaling [7] SVDA [Bretherton et al., 1992; Widmann, 2005; Tippett et al., 2008] was employed in order to unveil any relationship between variability in the station precipitation and that in the large-scale circulation. Before applying SVDA, the linear long-term trends in the JJA mean rainfall as well as the gridded reanalysis and hindcast data were removed to minimize the influence of any decadal changes. (Note that common hindcast period of 1983 2003 is within the same positive phase of PDO, such that the SVDA results should not be affected by any change of the PDO phase.) In this study, SLP was chosen as the large-scale variable (or predictor). This is because GCMs in general can reasonably capture the large-scale SLP variations over the Indo-Pacific region (figures not shown). Moreover, it has strong 5150

Figure 4. Normalized time series of the expansion coefficient for precipitation (solid line) and model SLP (dashed line) from (a) BCC, (b) CWB, (c) GCPS, (d) GDAPS, (e) MSC-GM2, (f) MSC-GM3, (g) MSC-SEF, (h) NCEP, (i) NIMR, (j) PNU, (k) POAMA, and (l) the MME average, corresponding to the leading SVD mode. covariability with the SC regional precipitation (predictand) (see Appendix A). Both the anomalous SLP and station precipitation can be expanded according to SVDA as follows: Precipitation ðt; xþ XN P i ðþq x i ðþ t i¼1 SLP ðt; xþ XN SLP j ðþr x j ðþ t j¼1 [8] Here the anomaly fields SLP(t,x) and precipitation(t,x) are normalized with unit standard deviation. N is the total number of SVD modes. SLP j (x) and P i (x) represent the singular vectors for the i th and j th SVD mode, while R j (t) and Q i (t) are the time expansion coefficients corresponding to the SLP and precipitation, respectively. Finally, for downscaling prediction, the following transfer function will be used: Predicted precipitation ðt; xþ X P i ðþr x j ðþb t ij i ¼ 1 j ¼ 1 where b ij are the coefficients relating precipitation and SLP. In general, [b ij ] is a matrix that can be determined by multiple linear regression (MLR) [Widmann, 2005; Tippett et al., 2008]. Chu et al. [2008] did not carry out MLR and approximated [b ij ] by a unit matrix (i.e., b ij equals 1 when i = j, N 5151

Figure 5. Correlation coefficients between the JJA precipitation at station locations based on observations and the interpolated DMO of precipitation from (a) BCC, (b) CWB, (c) GCPS, (d) GDAPS, (e) MSC-GM2, (f) MSC-GM3, (g) MSC-SEF, (h) NCEP, (i) NIMR, (j) PNU, (k) POAMA, and (l) the MME average. The correlation coefficient averaged over all stations is provided in the bottom right corner. otherwise b ij = 0). This is equivalent to saying that Q i and R j are perfectly correlated; hence, precipitation prediction is downscaled by multiplying the precipitation singular vectors with the corresponding SLP expansion coefficients. The expansion coefficients R j can be computed from the largescale circulation anomalies, which are supposed to be well captured by GCMs. In this study, statistical downscaling approaches with and without using MLR are evaluated and compared. Finally, N is set to be 18 in the SVDA expansion equation, although our results are practically independent of the number of modes if N > 15. 3. Results 3.1. Relationship Between SC Rainfall and Model Variables [9] SVDA between observed station precipitation and model SLP was first carried out, and the results for the leading mode are given in Figures 2 4. Figure 2 shows the leading precipitation pattern from SVDA with SLP taken from 11 different models and their MME average. It is worth mentioning that most of the singular vectors for rainfall compare well with observations (see Figure A1). We have 5152

Figure 6. Correlation coefficient difference between predictions based on non MLR-based statistical downscaling and DMO. also computed the pattern correlation between the observational and model results for the rainfall singular vector. Among the 11 models and MME, 9 of them give a pattern correlation higher than 0.6. From Figure 2, it can be seen that most models give strong rainfall-model SLP covariability with a pattern of surplus rainfall in coastal to eastern SC, and suppressed precipitation over the northwestern part of SC. The only exceptions are the NIMR model and POAMA. For NIMR, the strong positive rainfall signal in eastern SC is missing, while for POAMA, a clear eastern/coastal to western/inland dipole structure of rainfall cannot be found. The MME average also gives positive (negative) anomalous rainfall in eastern/coastal (western) SC, reflecting the leading rainfall pattern from the majority of models. Finally, this leading SVD mode explains about 50% or more of the squared covariance between observed rainfall and model SLP (with a maximum value of 77%) for all models except POAMA (which gives 38% of the fractional squared covariance). [10] The model SLP patterns for this SVD mode are given in Figure 3. In general, they are also consistent with observations, with most models giving a prominent low-pressure anomaly extending from Indochina to South China Sea. However, details of the SLP pattern are different from one model to another. For instance, only BCC and PNU models can capture the positive signal in northwestern SC. Moreover, the high-pressure system located over the Indian Ocean is not found in BCC, MSC-SEF, NIMR, and 5153

Figure 7. DMO. Same as Figure 6 except for the difference between MLR-based statistical downscaling and POAMA hindcasts. The MME average also gives a largescale low pressure anomaly over Indochina to SC, which is consistent with most models. Figure 4 gives the expansion coefficients for this SVD mode for each individual model and the MME average. The correlation between expansion coefficients of station precipitation and SLP is rather high for all models (ranging from 0.57 to 0.79). This is noteworthy because it indicates that the large-scale circulation in models is linked to the observed station precipitation. We also examined the second and third SVD modes between SC rainfall and model SLP. There is again relatively high correlation between the two sets of expansion coefficient time series (from the value of ~0.5 to 0.8), suggesting that the observed SC rainfall and the SLP field from model hindcasts have strong covariability. 3.2. Predictions Based on Direct Model Output and Statistical Downscaling [11] Before assessing the performance of models in predicting the local SC rainfall, DMO of precipitation was first interpolated onto each station location. The correlation between the observed and DMO precipitation at 89 stations is shown in Figure 5. The 89-station averaged correlation coefficient is also given at the bottom right of each panel. It is striking that many models show no skill in rainfall prediction in the western part of SC. The exceptions are the hindcasts from BCC and NCEP, which perform poorly in eastern or eastern-to-central SC. Finally, the MME mean gives the highest skill score, as can be seen from its high value of the correlation coefficient averaged over all stations. However, even for the MME average, the skill in some 5154

Figure 8. Difference between the temporal correlation coefficients for DMO and MLR downscaled precipitation for (a) type 1 and (b) type 2 model ensemble. western SC locations remains low; this might be related to the very low skill (and probably variance) of most models in this region. The performance of individual models can strongly affect the skill of MME, and hence, the MME technique cannot increase the skill in this particular subdomain. [12] Following the downscaling scheme outlined in section 2.2, the station-scale rainfall in SC was predicted based on model SLP outputs. Here the downscaled rainfall predictions were produced and validated based on a leave-one-out cross-validation framework. It involves making a single-year prediction with the target year excluded from the training period based on which the statistical scheme was constructed. This procedure was repeated each year, and the precipitation prediction was validated based on observations. The crossvalidated correlation coefficient was used to evaluate the performance of downscaling SC rainfall prediction based on both methods (with and without MLR). The difference between the correlation coefficients given by DMO and those from non MLR-based statistical downscaling is shown in Figure 6. For downscaling without MLR, the cross-validated correlation coefficient shows that most models perform better than DMO in many stations. For CWB and BCC, DMO gives the maximum correlation coefficient of just below 0.3 (see Figures 5a and 5b); downscaling can increase its value by ~0.4 or even more. For GCPS and GDAPS, there is an improvement in western SC, with the correlation coefficient increased by ~0.5 (see Figures 6c and 6d). Very similar improvement in the northwestern part of SC is also seen in NIMR, PNU, MSC-GM2, POAMA, and the MME mean hindcasts. For BCC and NCEP, the prediction skill is enhanced in the eastern part of SC. These results are consistent with previous studies showing that prediction skills can be greatly improved at locations where DMO performs poorly [Chen et al., 2012]. [13] Figure 7 shows the difference between correlation coefficients for DMO and those from MLR-based statistical downscaling. Again, rainfall prediction at some stations has better skill than DMO. For instance, downscaling can increase the correlation coefficient by about 0.4 in MSC- SEF. For GDAPS and POAMA, their 89-station averaged correlations are increased by 0.10 and 0.11, respectively. Similar enhancement of skill in the western part of SC is seen in other models except BCC and NCEP, which show improvement in eastern-coastal SC. Finally, the improvement of skill brought about by MLR-based downscaling is comparable to that due to the non MLR-based method. For the MME mean, MLR-based downscaling slightly outperforms that without MLR. (Besides temporal correlation, RMS error (RMSE) of DMO and the downscaling predictions were also computed. It was found that both downscaling methods give similar RMSE, but smaller than that for DMO (figures not shown).) Also note that there are a number of stations over which the statistical method underperforms DMO. Thus, depending on the location, statistical downscaling may or may not give better predictions compared to DMO (see also section 4). [14] Further inspection reveals that improvement is mainly seen in two separate regions. For one group of models (referred to as type 1 models), improvement is found in northwestern SC; they are CWB, GCPS, GDAPS, MSC-GM2, MSC-GM3, MSC-SEF, NIMR, PNU, and POAMA. For BCC, NCEP, and POAMA, referred to as type 2 models, improvement is found in the eastern part and coastal area of SC. (Note that POAMA belongs to both type 1 and type 2, meaning that its skill is improved substantially in both areas after downscaling.) Based on such classification, ensembles comprising type 1 and type 2 models were formed, and the respective rainfall prediction based on DMO and statistical downscaling were compared. The difference between the correlation coefficients for downscaling and DMO for type 1 and type 2 model ensembles are shown in Figures 8a and 8b, respectively. It is obvious that type 1 (type 2) model ensemble predictions from statistical downscaling perform much better than DMO in northwest (eastern and coastal) SC. Finally, we have also compared DMO and downscaling predictions using the mean square skill score (MSSS) [WMO, 2002]. MSSS ¼ 1 MSE for MSE clim where MSE for and MSE clim refer to the mean square error of the forecast and mean square error in model climatology, respectively. It can be seen that MSSS is zero if the forecast error equals to the error in climatology. If MSSS is positive, it indicates that MSE for is smaller than MSE clim. Conversely, a negative MSSS indicates MSE for that is higher than MSE clim. Marked improvement in MSSS is observed for both type 1 model (from 0.04 to 0.12) and type 2 model 5155

Figure 9. Regression coefficients of the JJA mean (a and b) rainfall (units: mm/d) and (c and d) SLP (contours in interval of 0.05 hpa, with negative values denoted by dashed lines) from (Figures 9a and 9c) observations and (Figures 9b and 9d) type 1 model ensemble average based on the second PC of the observed SC station rainfall. (from 0.05 to 0.17) brought about by MLR-based statistical downscaling for rainfall prediction in western and easterncoastal SC, respectively. [15] To shed light on how statistical downscaling can give better rainfall prediction in certain subregions in SC, we further examined the circulation features associated with the recurrent SC rainfall modes in both observations and model simulations. First, empirical orthogonal function (EOF) analysis of the observed SC rainfall was carried out. Then, both the observed and DMO data were regressed upon the second PC time series of SC rainfall from station observations. Figures 9a and 9b show the regression coefficients for the observed and type 1 model ensemble rainfall, respectively. (Note that the regression map for the observed rainfall has the same pattern as its second EOF.) It is noteworthy that this pattern resembles the leading singular vector of the SC rainfall (see Figure A1; the pattern correlation between the second EOF and the leading singular vector for rainfall is 0.74). In contrast, this rainfall pattern is not fully captured in the type 1 model environment. Corresponding to the same temporal variations of the observed second PC, models fail to predict suppressed rainfall in many of the inland SC stations (Figure 9b). Figures 9c and 9d give the corresponding regression coefficients for SLP. It can be seen that the second SC rainfall mode is associated with negative centers of action in northern Bay of Bengal and western north Pacific, and a positive anomaly in southwest China in observations (Figure 9c). This latter feature is consistent with the suppressed rainfall over the western to inland part of SC (see Figure 9a). On the other hand, such a positive SLP anomaly is not reproduced by the type 1 models (Figure 9d). This is probably the reason why there is no negative rainfall anomaly in the model ensemble. Finally, notice that the broad-scale features of the model SLP map are very similar to those from observations. This supports the notion that models have the ability to capture the large-scale circulation signals associated with the SC rainfall variations. Statistical techniques such as SVDA can map these circulation patterns to changes in the local rainfall, thereby producing skillful forecasts. For the type 2 models, similar analysis was also carried out by comparing the observed and model rainfall and SLP regression, based on the PCs of SC rainfall. Again the downscaling scheme can map the large-scale SLP anomaly and provides a bias-corrected rainfall prediction in the eastern and coastal SC locations (figures not shown). Downscaling can therefore enhance the rainfall prediction skill by type 2 models in this region. 4. Discussion and Summary [16] The relationship between summertime SC rainfall and the large-scale circulation over the Indo-Pacific sector has been studied, and a statistical downscaling scheme based on their covariability has been developed. SVDA was applied in order to examine the covariability between observed precipitation and model outputs. For the leading SVD mode, suppressed (enhanced) precipitation was found over the northwestern (eastern to southeastern) part of SC. This is accompanied by a large-scale SLP pattern with anomalously low pressure over Indochina and a large region in the western north Pacific. The correlation between expansion coefficients of station precipitation and model SLP is rather high (ranging from about 0.6 to 0.8). For the second and third singular vectors, there is still a strong resemblance with their observational counterpart in some models. [17] The above gave us the confidence to construct a statistical scheme for predicting the SC summertime rainfall based on SVDA. In particular, the performance of both MLR-based and non MLR-based downscaling prediction was assessed and compared to that from DMO. The 5156

latter was found to be skillful over some southern coastal locations, but otherwise, the skill is low in the inland region, especially over the western part of SC. On the other hand, the statistical downscaling schemes can greatly improve the prediction skill in western SC in most models; in coastal eastern SC, statistical downscaling also outperforms DMO for some models. We have also found that, for some models, non MLR-based downscaling gives better results than that using MLR. This is the case despite MLR being able to capture the maximum amount of variance. It is plausible that MLR-based downscaling may not give the best performance under the cross-validation framework. More work needs to be done to understand the skill of such statistical downscaling scheme for actual predictions. [18] Based on the area in which downscaling can improve the rainfall prediction, models were classified into two groups: for the type 1 models (including CWB, GCPS, GDAPS, MSC-GM2, MSC-GM3, MSC-SEF, NIMR, PNU, and POAMA), prediction over the western part of SC is improved significantly, while for type 2 models (including BCC, NCEP, and POAMA), the prediction skill is increased in eastern SC by statistical downscaling. Further analysis showed that, while models can reproduce the basin-scale circulation patterns associated with the recurrent SC rainfall modes, they have difficulties in capturing the details of the circulation pattern. For instance, type 1 models have skill in reproducing the regional circulation in the western north Pacific. However, these models have no skill in capturing the anomalous circulation pattern over western SC to Indochina, which can be important for the local rainfall variation. On the other hand, statistical downscaling can map the large-scale SLP patterns on local rainfall variability, thereby tapping the source of predictability from the large-scale circulation signals to enhance the prediction skill at some station locations. One way to utilize our result for actual forecasts, therefore, is to adopt statistical downscaling only in some selected regions. How DMO and statistical prediction products can be combined to improve the overall SC rainfall prediction should be further explored. [19] We have also made use of Z500 to develop statistical downscaling using the same SVDA technique. It was found that analysis based on the Z500 variable gives a set of rainfall singular vectors similar to those based on SLP. Despite the variable being well simulated by GCMs, the skill of Z500-based downscaling is lower. This is probably because the variable cannot fully capture circulation changes in the low latitudes. [20] Finally, we found that a number of models have considerable skill in capturing WNPSM activity (figures not shown). In view of its strong linkage with the circulation over SC, it seems likely that WNPSM is one major factor affecting the predictability of SC summertime rainfall. How WNPSM other climate models and PDO-related decadal variability affect the Asian monsoon rainfall and its predictability will be the subject of further studies. Appendix A: Relationship Between Rainfall and Large-Scale Circulation From Observations [21] Here the relationship between SC rainfall and the large-scale circulation from observations is examined. Based on correlation analyses between station precipitation and Figure A1. The dimensionless leading singular vector for (a) station precipitation and (b) SLP based on SVD analysis for station precipitation and SLP in JJA. Both rainfall and SLP fields are taken from observations. The upper right of Figure A1a shows the fraction of squared covariance between the two fields explained by this leading mode. (c) Normalized time series of the expansion coefficient for precipitation (solid line) and SLP (dashed line), corresponding to the leading SVD mode. Upper right shows the correlation coefficient between the two time series. various circulation variables, the area of 10ºS 35ºN and 60ºE 180ºE was adopted for SVDA. Figure A1 shows the precipitation and SLP singular vectors associated with the first SVD mode. This mode explains more than 50% 5157

of the squared covariance between station rainfall and SLP. The precipitation pattern indicates strong positive anomalies (solid circles) over the coastal SC and Hainan Island, whereas suppressed rainfall is found (open circles) over the northwestern part of SC (Figure A1a). Associated with this rainfall pattern, anomalous low pressure is found over Hainan, Vietnam, and Indochina, and a large-scale anomaly of the same sign is seen over the western north Pacific. In the north-western SC area, south of Japan, and over the Indian Ocean to Indonesia, high pressure anomalies can be seen (Figure A1b). The aforementioned high-pressure system in northwestern SC is consistent with the suppressed precipitation there. Also, over eastern to coastal SC, above normal precipitation is observed where the anomalous SLP is negative. In other words, the placement of the anomalous highs (lows) and the suppressed (enhanced) rainfall in SC are consistent with each other. [22] The standardized expansion coefficients for the station rainfall and SLP corresponding to this leading mode are given in Figure A1c. It is noteworthy that the two time series are highly correlated (with a correlation coefficient of 0.77), meaning that the SC precipitation is strongly coupled with large-scale SLP in the Indo-Pacific region. Note that the SLP pattern over the western north Pacific resembles the recurrent circulation associated with anomalous WNPSM activity [Wang et al., 2001]. In fact, the SLP expansion coefficient is highly correlated with the JJA Western North Pacific Monsoon Index (WNPMI, defined as the difference between the 850 hpa zonal wind averaged over 5ºN 15ºN, 100ºE 130ºE, and that over 20ºN 30ºN, 110ºE 140ºE) [Wang et al., 2001], with a correlation coefficient of 0.81. This indicates a strong relationship between SC rainfall and WNPSM activity, consistent with previous studies. [23] For the second SVD mode, which accounts for about 18% of the squared covariance, a pattern of suppressed (enhanced) rainfall at almost all stations is associated with positive (negative) anomalous SLP covering SC and Indochina (figure not shown). This mode is related to developing IOD events, as evidenced by the correlation of 0.52 between the SLP expansion coefficient and the JJA mean dipole mode index (DMI) [Saji and Yamagata, 2003]. The third SVD mode explains about 12% of the rainfall-slp covariability. It shows a connection between a northeast-to-southwest dipole SC precipitation pattern and SLP with a center of action covering Taiwan/western north Pacific (figure not shown). The correlations between rainfall and SLP expansion coefficients for the second and third mode are 0.62 and 0.66, respectively. Overall, it can be seen that there is strong covariability between SC rainfall and the large-scale circulation in the Indo-Pacific region. [24] Acknowledgments. The authors appreciate those institutes participating in the APCC multimodel ensemble operational system for providing the hindcast experiment data. We thank Prof. Joong-Bae Ahn and Drs. Congwen Zhu and Hongwen Kang for discussions. Tony Tung is supported by the City University of Hong Kong (grant 7002512). References Ashok, K., Z. Guan, N. H. Saji, and T. Yamagata (2004), Individual and combined influences of ENSO and the Indian Ocean Dipole on the Indian Summer Monsoon, J. Clim., 17, 3141 3155. Back, S. K., J. H. Ryu, and S. B. Ryoo (2002), Analysis of the CO2 doubling experiment using METRI AGCM. Part I: The characteristics of regional and seasonal climate response, J. Korean Meteorol. Soc., 38, 465 477. Bretherton, C. S., C. Smith, and J. M. Wallace (1992), An intercomparison of methods for finding coupled patterns in climate data, J. Clim., 5, 541 559. Cavazos, T. (1999), Large-scale circulation anomalies conductive to extreme precipitation events and derivation of daily rainfall in Northeastern Mexico and Southeastern Texas, J. Clim., 12, 1506 1523. Chang, C.-P., Y. Zhang, and T. Li (2000), Interannual and interdecadal variations of the East Asian Summer Monsoon and tropical Pacific SSTs. Part I: Roles of the subtropical ridge, J. Clim., 13, 4310 4325. Chen, J. Y. (1991), Analysis and Long-Term Prediction Research on Drought-Flood in China, p. 14, Chinese Agriculture Press, Beijing. Chen, H.-P., J.-Q. Sun, and H.-J. Wang (2012), A statistical downscaling model for forecasting summer rainfall in China from DEMETER hindcast datasets, Weather Forecast., 27, 608 628. Chu, J.-L., H. Kang, C.-Y. Tam, C.-K. Park, and C.-T. Chen (2008), Seasonal forecast for local precipitation over northern Taiwan using statistical downscaling, J. Geophys. Res., 113, doi:10.1029/2007jd009424. Ding, Y., Y. Ni, X. Zhang, W. Li, M. Dong, Z.-C. Zhao, Z. Li, and W. Shen (2000), Introduction to the Short-Term Climate Prediction Model System, p. 500, China Meteorological Press, Beijing, China. Doblas-Reyes, F. J., M. Deque, and J. P. Piedelievre (2000), Multi-model spread and probabilistic seasonal forecasts in PROVOST, Q. J. R. Meteorol. Soc., 126, 2069 2088. Gong, D.-Y., and C.-H. Ho (2002), Shift in the summer rainfall over the Yangtze River valley in the late 1970s, Geophys. Res. Lett., 29(10), 1436, 4, doi:10.1029/2001gl014523. Gong, D.-Y., and S.-W. Wang (1999), The response of global subtropical highs to the equatorial eastern Pacific SST anomaly, Acta Oceanol. Sin., 18, 203 214. Kanamitsu, M., W. Ebisuzaki, J. Woollen, S. K. Yang, J. J. Hnilo, M. Fiorino, and G. L. Poter (2002), NCEP-DOE AMIP-II reanalysis (R-2), Bull. Am. Meteorol. Soc., 83, 1631 1643. Kang, I.-S., J.-Y. Lee, and C.-K. Park (2004), Potential predictability of a dynamical seasonal prediction system with systematic error correction. J. Clim., 17, 834 844. Klein, W. H., B. M. Lewis, and I. Enger (1959), Objective prediction of five day mean temperature during winter, J. Meteorol., 16, 672 682. Krishnamurti, T. N., C. M. Kishtawal, T. E. LaRow, D. R. Bachiochi, Z. Zhang, C. E. Williford, S. Gadgil, and S. Surendran (1999), Improved weather and seasonal climate forecasts from multi-model superensemble. Science,285, 1548 1550, doi:10.1126/science.285.5433.1548. Lee, D.-Y., K. Ashok, and J.-B. Ahn (2011), Toward enhancement of prediction skills of multimodel ensemble seasonal prediction: A climate filter concept, J. Geophys. Res., 116, doi:10.1029/2010jd014610. Liou, C. S., J. H. Chen, C. T. Terng, F. J. Wang, C. T. Fong, T. E. Rosmond, H. C. Kuo, C. H. Shiao, and M. D. Cheng (1997), The second generation global forecast system at the central weather bureau in Taiwan, Weather Forecast., 3, 654 663. Liu, Y., K. Fan, and H.-J. Wang (2011), Statistical downscaling prediction of summer precipitation in Southeastern China, Atmos. Oceanic Sci. Lett., 4, 173 180. Lu, R., S.-R. Chan, and D. Buwen (2002), Association between the western north Pacific monsoon and the South China Sea Monsoon, Adv. Atmos. Sci., 19, 12 24. Mao, J. Y., J. C. L. Chan, and G. X. Wu (2011), Interannual variations of early summer monsoon rainfall over South China under different PDO backgrounds, Int. J. Climatol., 31(6), 847 862. Maraun, D., et al. (2010), Precipitation downscaling under climate change. Recent developments to bridge the gap between dynamical models and the end user, Rev. Geophys., 48, RG3003, doi:10.1029/2009rg000314. McFarlane, N. A., G. J. Boer, J. P. Blanchet, and M. Lazare (1992), The Canadian climate centre second generation circulation model and its equilibrium climate, J. Clim., 5, 1013 1044. Min, Y.-M., V. N. Kryjov, and J.-H. Oh (2011), Probabilistic interpretation of regression-based downscaled seasonal ensemble predictions with the estimation of uncertainty, J. Geophys. Res., 116, D08101, doi:10.1029/ 2010JD015284. Palmer, T. N., C. Brankovic, and D. S. Richardson (2000), A probability and decision-model analysis of PROVOST seasonal multi-model ensemble integrations, Q. J. R. Meteorol. Soc., 126(567), 2013 2033. Park, H., B. K. Park, D. K. Rha, and J. Y. Cho (2002), An improvement of global model in 2001, KMA/NWPD Tech. Rep., 2002 1. 5158

Ritchie, H. (1991), Application of the semi-lagrangian method to a multilevel spectral primitive-equations model, Q. J. R. Meteorol. Soc., 117, 91 106. Rodwell, D. P. (1998), Assessing potential seasonal predictability with an ensemble of multi-decadal GCM simulations, J. Clim., 11, 109 120. Saha, S., S. Nadiga, C. Thiaw, J. Wang, W. Wang, Q. Zhang, H. M. Van den Dool, H.-L.Pan,S.Moorthi,D.Behringer,D.Stokes,M.Peña,S.Lord,G.White,W. Ebisuzaki, P. Peng, P. Xie (2006), The NCEP climate forecast system, J. Clim., 19, 3483 3517. Saji, N. H., B. N. Goswami, P. N. Vinayachandran, and T. Yamagata (1999), A dipole mode in the tropical Indian Ocean, Nature, 401, 360 363. Saji, N. H., and T. Yamagata (2003), Possible impacts of Indian Ocean dipole mode events on global climate, Clim. Res., 25, 151 169. Scinocca, J. F., N. A. Mcfarlane, M. Lazare, J. Li, and D. Plummer (2008), The CCCma third generation AGCM and its extension into the middle atmosphere, Atmos. Chem. Phys., 8, 7055 7074. Shukla, J., et al. (2000), Dynamical seasonal prediction, Bull. Am. Meteorol. Soc., 81, 2493 2606, doi:10.1175/1520-0477(2000)081<2593: DSP>2.3.CO;2. Sun, J. Q., and J. B. Ahn, (2011), A GCM-based forecasting model for the landfall of tropical cyclones in China, Adv. Atmos. Sci., 28, 1049 1055. Sohn, S.-J., C.-Y. Tam, and C.-K. Park (2011), Leading modes of East Asian winter climate variability and their predictability: An assessment of the APCC multi-model ensemble, J. Meteorol. Soc. Jpn., 89, 455 474, doi:10.2151/jmsj.2011-504. Tang, W.-Y., and Z.-B. Sun (2005), Effect of IOD on East Asian circulation and precipitation, J. Nanjing Inst. Meteorol., 28(3), 316 322 (in Chinese). Thompson, D. W. J., and J. M. Wallace (2000), Annular modes in the extratropical circulation, part I: Month-to-month variability, J. Clim., 13, 1000 1016. Tippett, M. K., T. Delsole, S. J. Mason, and A. G. Barnston (2008), Regression-based methods for finding coupled patterns, J. Clim., 21, 4384 4398. von Storch, H., E. Zorita, and E. Cubasch (1993), Downscaling of global climate estimates to regional scales: An application to the Iberian rainfall in wintertime, J. Clim., 6, 1161 1171. Wang, B., R. Wu, and K.-M. Lau (2001), Interannual variability of the Asian summer monsoon: Contrasts between the Indian and the west north Pacific-east Asian monsoons, J. Clim., 14, 4073 4090. Wang, G.-C., and Z.-Y. Guan (2007), Modes of air-sea interactions in the Indian Ocean and their relationships with the rainfall over China as reveled by SVD analysis, J. Nanjing Inst. Meteorol., 30(1), 63 71 (in Chinese). Wetterhall, F., S. Halldin, and C.-Y. Xu (2005), Statistical precipitation downscaling in central Sweden with the analogue method, J. Hydrol., 306(1 4), 174 190. Widmann, M. (2005), One-dimensional CCA and SVD, and their relationship to regression maps, J. Clim., 18, 2785 2792. Widmann, M., C. S. Bretherton, and P. Salathé Eric (2003), Statistical precipitation downscaling over the Northwestern United States using numerically simulated precipitation as a predictor, J. Clim., 16, 799 816. Wilby, R. L., and T. M. Wigley (1997), Downscaling general circulation model: A review of methods and limitations, Prog. Phys. Geogr., 21(4), 530 548. Wilby, R. L., T. M. Wigley, D. Conway, P. D. Jones, B. C. Hewitson, J. Main, and D. S. Wilks (1998), Statistical downscaling of general circulation model output: A comparison of methods, Water Resour. Res., 34, 2295 3008. Wilby, R. L., S. P. Charles, E. Zorita, B. Timbal, P. Whetton, and L. O. Mearns (2004), Guidelines for use of climate scenarios developed from statistical downscaling methods, Intergovernmental Panel on Climate Change Task Group on Data and Scenario Support for Impact and Climate Analysis. Wilks, D. S. (1995), Statistical Methods in the Atmospheric Sciences, p. 467, Academic Press, San Diego, Calif. WMO (2002), Standardized verification system (SVS) for long-range forecasts (LRF), Manual on the GDPS, WMO-No. 485, 1. Wu, R., and B. Wang (2000), Interannual variability of summer monsoon onset over the western north Pacific and the underlying processes, J. Clim., 13, 2483 2501. Yu, R.-C., M.-H. Zhang, Y.-Q. Yu, and Y.-M. Liu (2001), Summer monsoon rainfalls over Mid-Eastern China lagged correlated with global SSTS, Adv. Atmos. Sci., 18, 179 196. Zhao, S., T. Zhou, X. Yang, Y. Zhu, Y. Tan, and X. Sun (2011), Interdecadal change of the relationship between the tropical Indian Ocean dipole mode and the summer climate anomaly in China, Acta Meteorol. Sin., 25, 129 141. Zhong, A., H. H. Hendon, and O. Alves (2005), Indian Ocean variability and its association with ENSO in a global coupled model, J. Clim., 18, 3634 3649. Zhu, C., C.-K. Park, W.-S. Lee, and W.-T. Yu (2008), Statistical downscaling for multi-model ensemble prediction of summer monsoon rainfall in the Asia-Pacific region using geopotential height field, Adv. Atmos. Sci., 25, 867 884. Zorita, E., and H. von Storch (1999), The analog method as a simple statistical downscaling technique: Comparison with more complicated methods, J. Clim., 12, 2474 2489. 5159