Influence of Future Tropical Cyclone Track Changes on Their Basin-Wide Intensity over the Western North Pacific: Downscaled CMIP5 Projections

The possible changes of tropical cyclone（TC） tracks and their influence on the future basin-wide intensity of TCs over the western North Pacific（WNP） are examined based on the projected large-scale environments derived from a selection of CMIP5（Coupled Model Intercomparison Project Phase 5） models. Specific attention is paid to the performance of the CMIP5 climate models in simulating the large-scale environment for TC development over the WNP. A downscaling system including individual models for simulating the TC track and intensity is used to select the CMIP5 models and to simulate the TC activity in the future.The assessment of the future track and intensity changes of TCs is based on the projected large-scale environment in the21 st century from a selection of nine CMIP5 climate models under the Representative Concentration Pathway 4.5（RCP4.5）scenario. Due to changes in mean steering flows, the influence of TCs over the South China Sea area is projected to decrease,with an increasing number of TCs taking a northwestward track. Changes in prevailing tracks and their contribution to basin-wide intensity change show considerable inter-model variability. The influences of changes in prevailing track make a marked contribution to TC intensity change in some models, tending to counteract the effect of SST warming. This study suggests that attention should be paid to the simulated large-scale environment when assessing the future changes in regional TC activity based on climate models. In addition, the change in prevailing tracks should be considered when assessing future TC intensity change.

Climate Change Impact on Wheat Production in the Southern Great Plains of the US Using Downscaled Climate Data

Gradually developing climatic and weather anomalies due to increasing concentration of atmospheric greenhouse gases can pose threat to farmers and resource managers. There is a growing need to quantify the effects of rising temperature and changing climates on crop yield and assess impact at a finer scale so that specific adaptation strategies pertinent to that location can be developed. Our work aims to quantify and evaluate the influence of future climate anomalies on winter wheat (Triticum aestivum L.) yield under the Representative Concentration Pathways 6.0 and 8.5 using downscaled climate projections from different General Circulation Models (GCMs) and their ensemble. Marksim downscaled daily data of maximum (TMax) and minimum (TMin) air temperature, rainfall, and solar radiation (SRAD) from different Coupled Model Intercomparison Project GCMs (CMIP5 GCMs) were used to simulate the wheat yield in water and nitrogen limiting and non-limiting conditions for the future period of 2040-2060. The potential impact of climate changes on winter wheat production across Oklahoma was investigated. Climate change predictions by the downscaled GCMs suggested increase in air temperature and decrease in total annual rainfall. This will be really critical in a rainfed and semi-arid agro-ecological region of Oklahoma. Predicted average wheat yield during 2040-2060 increased under projected climate change, compared with the baseline years 1980-2014. Our results indicate that downscaled GCMs can be applied for climate projection scenarios for future regional crop yield assessment.

Applying Downscaled Global Climate Model Data to a Groundwater Model of the Suwannee River Basin, Florida, USA

The application of Global Climate Model (GCM) output to a hydrologic model allows for comparisons between simulated recent and future conditions and provides insight into the dynamics of hydrology as it may be affected by climate change. A previously developed numerical model of the Suwannee River Basin, Florida, USA, was modified and calibrated to represent transient conditions. A simulation of recent conditions was developed for the 372-month period 1970-2000 and was compared with a simulation of future conditions for a similar-length period 2039-2069, which uses downscaled GCM data. The MODFLOW groundwater-simulation code was used in both of these simulations, and two different MODFLOW boundary condition “packages” (River and Streamflow-Routing Packages) were used to represent interactions between surface-water and groundwater features. The hydrologic fluxes between the atmosphere and landscape for the simulation of future conditions were developed from dynamically downscaled precipitation and evapotranspiration (ET) data generated by the Community Climate System Model (CCSM). The downscaled precipitation data were interpolated for the Suwannee River model grid, and the downscaled ET data were used to develop potential ET and were interpolated to the grid. The future period has higher simulated rainfall (10.8 percent) and ET (4.5 percent) than the recent period. The higher future rainfall causes simulated groundwater levels to rise in areas where they are deep and have little ET in either the recent or future case. However, in areas where groundwater levels were originally near the surface, the greater future ET causes groundwater levels to become lower despite the higher projected rainfall. The general implication is that unsaturated zone depth could be more spatially uniform in the future and vegetation that requires a range of conditions (substantially wetter or drier than average) could be detrimentally affected. This vegetation would include wetland species, especially in areas inland from the coast.

Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks

This study assesses the suitability of convolutional neural networks(CNNs) for downscaling precipitation over East Africa in the context of seasonal forecasting. To achieve this, we design a set of experiments that compare different CNN configurations and deployed the best-performing architecture to downscale one-month lead seasonal forecasts of June–July–August–September(JJAS) precipitation from the Nanjing University of Information Science and Technology Climate Forecast System version 1.0(NUIST-CFS1.0) for 1982–2020. We also perform hyper-parameter optimization and introduce predictors over a larger area to include information about the main large-scale circulations that drive precipitation over the East Africa region, which improves the downscaling results. Finally, we validate the raw model and downscaled forecasts in terms of both deterministic and probabilistic verification metrics, as well as their ability to reproduce the observed precipitation extreme and spell indicator indices. The results show that the CNN-based downscaling consistently improves the raw model forecasts, with lower bias and more accurate representations of the observed mean and extreme precipitation spatial patterns. Besides, CNN-based downscaling yields a much more accurate forecast of extreme and spell indicators and reduces the significant relative biases exhibited by the raw model predictions. Moreover, our results show that CNN-based downscaling yields better skill scores than the raw model forecasts over most portions of East Africa. The results demonstrate the potential usefulness of CNN in downscaling seasonal precipitation predictions over East Africa,particularly in providing improved forecast products which are essential for end users.

Scientific Advances and Weather Services of the China Meteorological Administration’s National Forecasting Systems during the Beijing 2022 Winter Olympics

Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.

Carbon efficiency evaluation method for urban energy system with multiple energy complementary

Urban energy systems(UESs)play a pivotal role in the consumption of clean energy and the promotion of energy cascade utilization.In the context of the construction and operation strategy of UESs with multiple complementary energy resources,a comprehensive assessment of the energy efficiency is of paramount importance.First,a multi-dimensional evaluation system with four primary indexes of energy utilization,environmental protection,system operation,and economic efficiency and 21 secondary indexes is constructed to comprehensively portray the UES.Considering that the evaluation system may contain a large number of indexes and that there is overlapping information among them,an energy efficiency evaluation method based on data processing,dimensionality reduction,integration of combined weights,and gray correlation analysis is proposed.This method can effectively reduce the number of calculations and improve the accuracy of energy efficiency assessments.Third,a demonstration project for a UES in China is presented.The energy efficiency of each scenario is assessed using six operational scenarios.The results show that Scenario 5,in which parks operate independently and investors build shared energy-storage equipment,has the best results and is best suited for green and low-carbon development.The results of the comparative assessment methods show that the proposed method provides a good energy efficiency assessment.This study provides a reference for the optimal planning,construction,and operation of UESs with multiple energy sources.

Seasonal Prediction of Indian Summer Monsoon Using WRF: A Dynamical Downscaling Perspective

Seasonal forecasting of the Indian summer monsoon by dynamically downscaling the CFSv2 output using a high resolution WRF model over the hindcast period of 1982-2008 has been performed in this study. The April start ensemble mean of the CFSv2 has been used to provide the initial and lateral boundary conditions for driving the WRF. The WRF model is integrated from 1st May through 1st October for each monsoon season. The analysis suggests that the WRF exhibits potential skill in improving the rainfall skill as well as the seasonal pattern and minimizes the meteorological errors as compared to the parent CFSv2 model. The rainfall pattern is simulated quite closer to the observation (IMD) in the WRF model over CFSv2 especially over the significant rainfall regions of India such as the Western Ghats and the central India. Probability distributions of the rainfall show that the rainfall is improved with the WRF. However, the WRF simulates copious amounts of rainfall over the eastern coast of India. Surface and upper air meteorological parameters show that the WRF model improves the simulation of the lower level and upper-level winds, MSLP, CAPE and PBL height. The specific humidity profiles show substantial improvement along the vertical column of the atmosphere which can be directly related to the net precipitable water. The CFSv2 underestimates the specific humidity along the vertical which is corrected by the WRF model. Over the Bay of Bengal, the WRF model overestimates the CAPE and specific humidity which may be attributed to the copious amount of rainfall along the eastern coast of India. Residual heating profiles also show that the WRF improves the thermodynamics of the atmosphere over 700 hPa and 400 hPa levels which helps in improving the rainfall simulation. Improvement in the land surface fluxes is also witnessed in the WRF model.

Projection of China's Near- and Long-Term Climate in a New High-Resolution Daily Downscaled Dataset NEX-GDDP

The projection of China＇s near- and long-term future climate is revisited with a new-generation statistically down- scaled dataset, NEX-GDDP （NASA Earth Exchange Global Daily Downscaled Projections）. This dataset presents a high-resolution seamless climate projection from 1950 to 2100 by combining observations and GCM results, and re- markably improves CMIP5 hindcasts and projections from large scale to regional-to-local scales with an unchanged long-term trend. Three aspects are significantly improved： （1） the climatology in the past as compared against the ob- servations; （2） more reliable near- and long-term projections, with a modified range of absolute value and reduced inter-model spread as compared to CMIP5 GCMs; and （3） much added value at regional-to-local scales compared to GCM outputs. NEX-GDDP has great potential to become a widely-used high-resolution dataset and a benchmark of modem climate change for diverse earth science communities.

Future meteorological drought conditions in southwestern Iran based on the NEX-GDDP climate dataset

Investigation of the climate change effects on drought is required to develop management strategies for minimizing adverse social and economic impacts.Therefore,studying the future meteorological drought conditions at a local scale is vital.In this study,we assessed the efficiency of seven downscaled Global Climate Models(GCMs)provided by the NASA Earth Exchange Global Daily Downscaled Projections(NEX-GDDP),and investigated the impacts of climate change on future meteorological drought using Standard Precipitation Index(SPI)in the Karoun River Basin(KRB)of southwestern Iran under two Representative Concentration Pathway(RCP)emission scenarios,i.e.,RCP4.5 and RCP8.5.The results demonstrated that SPI estimated based on the Meteorological Research Institute Coupled Global Climate Model version 3(MRI-CGCM3)is consistent with the one estimated by synoptic stations during the historical period(1990-2005).The root mean square error(RMSE)value is less than 0.75 in 77%of the synoptic stations.GCMs have high uncertainty in most synoptic stations except those located in the plain.Using the average of a few GCMs to improve performance and reduce uncertainty is suggested by the results.The results revealed that with the areas affected by wetness decreasing in the KRB,drought frequency in the North KRB is likely to increase at the end of the 21st century under RCP4.5 and RCP8.5 scenarios.At the seasonal scale,the decreasing trend for SPI in spring,summer,and winter shows a drought tendency in this region.The climate-induced drought hazard can have vast consequences,especially in agriculture and rural livelihoods.Accordingly,an increasing trend in drought during the growing seasons under RCP scenarios is vital for water managers and farmers to adopt strategies to reduce the damages.The results of this study are of great value for formulating sustainable water resources management plans affected by climate change.

Predictor Selection for CNN-based Statistical Downscaling of Monthly Precipitation

Convolutional neural networks(CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation models(GCMs).However, there is a lack of research exploring the predictor selection for CNN modeling. This paper presents an effective and efficient greedy elimination algorithm to address this problem. The algorithm has three main steps: predictor importance attribution, predictor removal, and CNN retraining, which are performed sequentially and iteratively. The importance of individual predictors is measured by a gradient-based importance metric computed by a CNN backpropagation technique, which was initially proposed for CNN interpretation. The algorithm is tested on the CNN-based statistical downscaling of monthly precipitation with 20 candidate predictors and compared with a correlation analysisbased approach. Linear models are implemented as benchmarks. The experiments illustrate that the predictor selection solution can reduce the number of input predictors by more than half, improve the accuracy of both linear and CNN models,and outperform the correlation analysis method. Although the RMSE(root-mean-square error) is reduced by only 0.8%,only 9 out of 20 predictors are used to build the CNN, and the FLOPs(Floating Point Operations) decrease by 20.4%. The results imply that the algorithm can find subset predictors that correlate more to the monthly precipitation of the target area and seasons in a nonlinear way. It is worth mentioning that the algorithm is compatible with other CNN models with stacked variables as input and has the potential for nonlinear correlation predictor selection.

Evaluation of Performance of Polar WRF Model in Simulating Precipitation over Qinghai-Tibet Plateau

Considering the complex topographic forcing and large cryosphere concentration,the present study utilized the polar-optimized WRF model(Polar WRF)to conduct downscaling simulations over the Qinghai-Tibet Plateau(TP)and its surrounding regions.Multi-group experiments with the 10 km horizontal resolution are used to evaluate the modeling of precipitation.Firstly,on the basis of the model ground surface properties upgrade and the optimized Noah-MP,the“better-performing”configuration suite for modeling precipitation is comprehensively examined.Various model parameters such as nudging options,five cumulus parameterization schemes,two planetary boundary layer schemes,and six microphysics schemes are investigated to further refine the Polar WRF configuration.Moreover,the precipitation simulation for a full calendar year is compared with multiple reanalyses and observations.The simulations demonstrate that the Polar WRF model successfully captures the general features of precipitation over this region and is sensitive to model parameters.Based on the results,it is recommended to use grid nudging with q intensity coefficient of 0.0002,the multi-scale kain-fritsch cumulus parameterization,the Yonsei University boundary layer scheme,and the Morrison 2-mom microphysics with reduced default droplet concentration value of 100 cm-3.Overall,the model performance is better than the ERA-interim and TRMM 3b42.It is comparable to,and in some cases slightly better than,the CRA-Land,especially in the prediction for the western part of the plateau where in situ observations are limited,and the cryosphere-atmosphere interaction is more pronounced.

Projecting future precipitation change across the semi-arid Borana lowland,southern Ethiopia

Climate change caused by past,current,and future greenhouse gas emissions has become a major concern for scientists in the field in many countries and regions of the world.This study modelled future precipitation change by downscaling a set of large-scale climate predictor variables(predictors)from the second generation Canadian Earth System Model(CanESM2)under two Representative Concentration Pathway(RCP)emission scenarios(RCP4.5 and RCP8.5)in the semi-arid Borana lowland,southern Ethiopia.The Statistical DownScaling Model(SDSM)4.2.9 was employed to downscale and project future precipitation change in the middle(2036-2065;2050s)and far(2066-2095;2080s)future at the local scale.Historical precipitation observations from eight meteorological stations stretching from 1981 to 1995 and 1996 to 2005 were used for the model calibration and validation,respectively,and the time period of 1981-2018 was considered and used as the baseline period to analyze future precipitation change.The results revealed that the surface-specific humidity and the geopotential height at 500 hPa were the preferred large-scale predictors.Compared to the middle future(2050s),precipitation showed a much greater increase in the far future(2080s)under both RCP4.5 and RCP8.5 scenarios at all meteorological stations(except Teletele and Dillo stations).At Teltele station,the projected annual precipitation will decrease by 26.53%(2050s)and 39.45%(2080s)under RCP4.5 scenario,and 34.99%(2050s)and 60.62%(2080s)under RCP8.5 scenario.Seasonally,the main rainy period would shift from spring(March to May)to autumn(September to November)at Dehas,Dire,Moyale,and Teltele stations,but for Arero and Yabelo stations,spring would consistently receive more precipitation than autumn.It can be concluded that future precipitation in the semi-arid Borana lowland is predicted to differ under the two climate scenarios(RCP4.5 and RCP8.5),showing an increasing trend at most meteorological stations.This information could be helpful for policymakers to design adaptation plans in water resources management,and we suggest that the government should give more attention to improve early warning systems in drought-prone areas by providing dependable climate forecast information as early as possible.

Statistical Downscaling Retrieval of Land Surface Temperature in an Area with Complex Landforms in the Eastern Qinling Mountains of China Based on Sentinel-2/3 Satellite Data

The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal resolutions in extracting LST from satellite remote sensing(RS)data,the areas with complex landforms of the Eastern Qinling Mountains were selected as the research targets to establish the correlation between the normalized difference vegetation index(NDVI)and LST.Detailed information on the surface features and temporal changes in the land surface was provided by Sentinel-2 and Sentinel-3,respectively.Based on the statistically downscaling method,the spatial scale could be decreased from 1000 m to 10 m,and LST with a Sentinel-3 temporal resolution and a 10 m spatial resolution could be retrieved.Comparing the 1 km resolution Sentinel-3 LST with the downscaling results,the 10 m LST downscaling data could accurately reflect the spatial distribution of the thermal characteristics of the original LST image.Moreover,the surface temperature data with a 10 m high spatial resolution had clear texture and obvious geomorphic features that could depict the detailed information of the ground features.The results showed that the average error was 5 K on April 16,2019 and 2.6 K on July 15,2019.The smaller error values indicated the higher vegetation coverage of summer downscaling result with the highest level on July 15.

全球气候变化对区域水资源影响研究进展综述

气候变化将改变全球水循环的现状,导致水资源时空分布的重新分配,并对降水、蒸散发、径流等造成直接影响。国内外学者越来越重视气候变化对区域水资源影响的研究,但是研究中存在着薄弱环节,首先是气候模型和水文模型耦合中出现的不精确问题,其次是研究主要集中在气候变化对区域平均径流变化的影响。深入分析存在的不足之处,旨在综合国内外研究经验,促进我国相关研究的发展。

Numerical and physical simulations of array laterolog in deviated anisotropic formation

Due to the tremendous amount of high-resolution measurement information,array laterolog is widely used in evaluations of deviated anisotropic reservoirs.However,the precision of a complementary numerical simulation should be improved as high as the core of fine-scale reservoir evaluation.Therefore,the 3D finite element method(3D-FEM)is presented to simulate the array laterolog responses.Notably,a downscaled physical simulation system is introduced to validate and calibrate the precision of the 3D-FEM.First,the size of the downscaled system is determined by COMSOL.Then,the surrounding and investigated beds are represented by a sodium chloride solution and planks soaked in solution,respectively.Finally,a half-space measurement scheme is presented to improve the experimental efficiency.Moreover,the corresponding sensitivity function and separation factor are established to analyze the effects of the formation anisotro py and dipping angle on the array laterolog responses.The numerical and experimental results indicate that the half-space method is practical,and the mean relative error between the numerical and experimental results is less than 5%,which indicates that the numerical simulation is accurate.With the proposed approach,the reversal angle of array laterolog response curves in anisotropic formations can be observed,and this range is determined to be 50°-62°.

降尺度方法中的初始资料处理的研究

从 GCM模式预测结果获取区域尺度特征的气候变化的统计方法被称为 Downscaling方法 ,它主要是通过区域气候尺度的预报量与 GCM模式输出或大尺度地面观测资料建立统计模式。在建立模式之初 ,研究各种尺度资料的匹配是至关重要的问题。本文在不同的站点数量、区域网格分辨率和差值影响半径的条件下 ,对资料处理方法的可行性和结果的可靠性作了分析探讨。结果说明 :一个大尺度网格箱内用 2 0个站点资料代替 4 0个站点的资料是可行的 ,并且确定了最佳插值方案 :当次网格分辨率取 1°× 1°时 ,插值影响半径应取 1;当次网格分辨率取 0 .5°× 0 .5°时 ,插值影响半径应取为 2。本文还对温度场、降水场及其对应的 EOF场进行了比较试验 ;并做了温度场、降水场与同步海平面气压场 (SL P)

饶河流域未来水资源量变化预测分析

利用景德镇气象站1961-2001年的实测降水、气温数据以及NCEP再分析数据,建立饶河流域降水、气温的SDSM统计降尺度模型;根据IPCC AR4排放情景特别报告中的A2和B2情景,对HADCM3输出数据进行降尺度处理,预测饶河流域未来时段(2010-2099年)的降水、气温变化情况;与新安江模型进行耦合,得到未来时段饶河流域的水资源量。结果表明:饶河流域未来水资源量持续减少,且A2情景比B2情景的降幅更大,至2080s时期(2070-2099年)昌江支流最大降幅可达31.01%。

Statistical Downscaling for Multi-Model Ensemble Prediction of Summer Monsoon Rainfall in the Asia-Pacific Region Using Geopotential Height Field

The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.