ATMOSPHERIC AND OCEANIC SCIENCE LETTERS, 2011, VOL. 4, NO. 3, 173180
Statistical Downscaling Prediction of Summer Precipitation in
Southeastern China
LIU Ying1, 2, FAN Ke1, and WANG Hui-Jun1
1
2
Nansen-Zhu International Research Centre, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
Graduate University of the Chinese Academy of Sciences, Beijing 100049, China
Received 25 January 2011; revised 22 February 2011; accepted 22 February 2011; published 16 May 2011
Abstract A statistical downscaling approach based on
multiple-linear-regression (MLR) for the prediction of
summer precipitation anomaly in southeastern China was
established, which was based on the outputs of seven operational dynamical models of Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER) and observed data. It
was found that the anomaly correlation coefficients
(ACCs) spatial pattern of June-July-August (JJA) precipitation over southeastern China between the seven
models and the observation were increased significantly;
especially in the central and the northeastern areas, the
ACCs were all larger than 0.42 (above 95% level) and
0.53 (above 99% level). Meanwhile, the root-mean-square
errors (RMSE) were reduced in each model along with
the multi-model ensemble (MME) for some of the stations
in the northeastern area; additionally, the value of RMSE
difference between before and after downscaling at some
stations were larger than 1 mm d1. Regionally averaged
JJA rainfall anomaly temporal series of the downscaling
scheme can capture the main characteristics of observation, while the correlation coefficients (CCs) between the
temporal variations of the observation and downscaling
results varied from 0.52 to 0.69 with corresponding variations from 0.27 to 0.22 for CCs between the observation
and outputs of the models.

Keywords: statistical downscaling, DEMETER, southeastern China, summer precipitation anomaly
Citation: 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.
1
Introduction
Southeastern China is economically well developed,
has a dense population, and experiences more floods in
the spring and summer seasons. The climate anomaly can
threaten millions of lives and property (Chen, 1991). The
prediction of precipitation during June-July-August (JJA)
over southeastern China is difficult due to many uncertainties. The major question in this context is regarding
the method of employment of the available model outputs
to increase prediction skill effectively. It is widely accepted that Atmospheric and Ocean Coupled General
Circulation Models (AOGCM) models are able to reasonably simulate large-scale atmospheric characters, such
Corresponding author: LIU Ying, [email protected]
as geopotential height at the highest level, temperature
near the surface and atmospheric circulation (Von Storch
et al., 1993). However, the resolution of AOGCM output
is too coarse to provide regional-scale information required for regional impact assessments (Fan et al., 2005).
Our research question is regarding the method to improve
the accuracy of the AOGCM output in our study region.
At present, there are the following two main methods
available to make up for the lack of regional scale climate
change of AOGCM: a high-resolution AOGCM (Boyle,
1993); and the downscaling method (Kang et al., 2009).
As the former approach requires a considerable amount of
computer resources for increasing the resolution of
AOGCM, the latter approach is becoming more popular.
Downscaling methods can be classified into the following three main groups: Regional climate models
nested in AOGCM, statistical downscaling, and dynamical-statistical downscaling. Due to convenience, relative
simplicity, and less sources, the statistical downscaling
method is applied widely. Essentially, the idea of the statistical downscaling technique consists of seeking relationships between the predictors (large-scale variables,
i.e., geopotential height (GPH) at 500 hPa, sea-surface
pressure, and SST) and the predictands (regional-scale
variables, i.e., local rainfall and temperature) to set up
statistical models(Cavazos and Hewitson, 2005; Paul et
al., 2008).
Many researchers use different linear statistical methods to downscale interested meteorological elements (Zorita et al., 1992; Widmann and Bretherton, 2003). Multiple-linear-regression (MLR) is one of the linear statistical
downscaling methods. Its principal technique is to find
regression links between multi-predictors and predictands.
Many recent studies have applied different statistical
downscaling methods to predict regional precipitation or
temperature (Fan et al., 2009; Wang and Fan, 2009).
Most previous studies revealed that precipitation and
temperature are the most effectively predictable by selecting different predictors as per the different regions.
However, the dataset of the Development of a European
Multi-model Ensemble System for Seasonal to Interannual Prediction (DEMETER) has not been applied to
study summer rainfall over southeastern China by employing statistical downscaling. For this purpose, we have
chosen the MLR using hindcast results of the seven models in the DEMETER, to concentrate on JJA precipitation
anomaly prediction. The datasets employed in this study
are described briefly in section 2, and the results are pre-
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ATMOSPHERIC AND OCEANIC SCIENCE LETTERS
sented in section 3. Section 4 presents conclusions and
discussions based on the results.
2
2.1
Data and methodology
Data
The DEMETER project has been funded by the European Union for the period of April 2000 to September
2003. The DEMETER system comprises of seven global
coupled ocean-atmosphere models initiated on 1 February,
1 May, 1 August, and 1 November initial conditions. Each
hindcast of the models has been integrated for six months
and comprises of an ensemble of nine members (Palmer
et al., 2004).
The models used in the DEMETER project are those of
CERFACS (European Centre for Research and Advanced
Training in Scientific Computation, France), CNRM
(Centre National de Recherches Météorologiques, France),
ECMWF (European Centre for Medium-Range Weather
Forecasts, UK), INGV (Istituto Nazionale de Geofisica e
Vulcanologia, Italy), LODYC (Laboratoire d’Océanographie Dynamique et de Climatologie, France), MPI
(Max-Planck Institut für Meteorologie, Germany), and
UKMO (Met Office, UK). In this paper, the multi-model
ensemble (MME) is defined as the average ensemble of
these seven models (More detailed information about the
DEMETER can be found at the following website: http://
www.ecmwf.int/research/demeter/general/index.html).
The monthly rainfall, specific humidity at 850 hPa
(q850) and the geopotential height at 500 hPa (Z500) datasets in JJA for the period of 19802001 in the DEMETER
project were applied. Moreover, for accuracy, the datasets
of the DEMETER starting from 1 May were chosen. The
monthly SST data was HadISST 1.1 monthly average sea
surface temperature (HadISST1), which was obtained
from the Met Office Hadley Centre, and it was composed
of a 1-degree latitude by 1-degree longitude grid of a
globally complete dataset. Additionally, 700 hPa GPH
(Z700) in ECMWF 40-yr Reanalysis (ERA-40) were employed. The source of the observed rainfall data at 753
stations over China from 1980 to 2001 were obtained
from the China Meteorological Administration National
Meteorological Information Center. Antarctic Oscillation
index (AAOI) was defined as the time coefficient series
of the first mode of empirical orthogonal function (EOF)
analysis of Z700 pole ward of 20S (Thompson and Wallace, 1998). In this paper, the southeastern China region is
located in 2530°N, 110122°E and contained 59 stations.
2.2
VOL. 4
Methodology
The precipitation in southeastern China has a close relationship with large-scale background atmospheric circulation and SST. When the weakening of the South
China Sea sub-high is delayed, abnormal floods prevail
from the middle and lower reaches of the Yangtze River
up to southeastern China (Wan et al., 2008). SST in a key
region, for example, northern Atlantic and tropical eastern
Pacific, is also an important factor influencing southeastern China precipitation, while the central region of southeastern China is the most affected by the SST (Chen et al.,
2003; Lang and Wang, 2010; Wang and Chen, 2004).
Abnormal atmospheric circulation over the middle and
lower reaches of the Yangtze River and southeastern
China followed by rainstorm and heavy rainstorm in these
regions led to the invalidation of climate prediction in the
summer of 1999 (Wang and Zhang, 2000). Mid-high latitude at 500 hPa geopotential height is another key factor
that influences summer precipitation over eastern China
(Pan et al., 2004; Wang, 2000; Zhou and Wang, 2006).
Disturbance in mid-latitude atmosphere is an effective
factor for the precipitation in eastern China (Huang et al.,
2003).
Water vapor in the South China Sea (SCS), the Bay of
Bengal, and the southwestern side of the western Pacific
subtropical high are the key regions that influence China’s
summer monsoon precipitation. The vapor fluxes merge
in the Yangtze River basin, which forces summer rainfall
in this area (Huang et al., 1998). An increase of the vapor
flux in the SCS and the Bay of Bengal will increase rainfall in south and southeastern China (Liu, 2006).
AAO is the key factor that impacts the climate and
can play an important part in JJA precipitation in the
Yangtze River and the Jianghuai River basin (Gao et al.,
2003; Wang and Fan, 2005; Fan, 2006; Fan and Wang,
2006; Sun et al., 2009; Zhu, 2009). Therefore, for this
paper, we chose the current spring AAOI as a predictor.
Based on previous research, we chose q850 as one of
predictors in the Asian JJA monsoon region. The other
predictors were area-average December-January-February
(DJF) SST and area-average Z500 (JJA). The influential
areas of these predictors are chosen by the most significant correlation coefficients (CCs) between the precipitation (JJA) anomaly in southeastern China and the global
fields of Z500 (JJA), q850 (JJA), and SST (DJF). Predictors
are described in Table 1. The correlation coefficients’ relationship between the predictors and the regionally averaged JJA precipitation over southeastern China for the
period of 19802001 are listed in Table 2. The results
Table 1 Predictors and their key regions where the correlation coefficients between regionally average summer precipitation over southeastern
China and global predictors were significant.
Predictors
Key regions
Z500 (JJA) of models
Baikal region (37.555°N, 80130°E)
q850 (JJA) of models
East Asian summer monsoon region (530°N, 60160°E)
Kuroshio region (20.534.5°N, 120.5140.5°E),
North Atlantic (30.545.5°N, 40.5°W10.5°E)
South Pacific (19.54.5°S, 105.5170.5°W)
Observed SST (DJF)
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LIU ET AL.: STATISTICAL DOWNSCALING, SUMMER PRECIPITATION, SOUTHEASTERN CHINA
Table 2 Correlation coefficients between the predictors at the different
regions of observation and regionally averaged JJA precipitation in
southeastern China during 19802001.
Z500
q850
AAOI
SSTKU
SSTNA
SSTSP
0.50
0.39
0.54
0.56
0.61
0.40
The underlined CCs indicate above 90% significance level. SSTKU,
SSTNA, and SSTSP mean sea surface temperature in the areas of Kuroshio,
North Atlantic and South Pacific.
indicate that the correlation coefficients of all predictors
are significant.
The downscaling technique in this paper is carried by
using the leaving-one-year-out cross validation method
(Michaelsen, 1987). Every target year was picked out
from the 22-year period (19802001), the MLR fitting
equations between the predictors and predictands were
carried out for the residual 21-year period, and every year
acted as a predictand for one year. In addition, we established a MLR equation for every station in the southeastern China region. This MLR equation is based on the previous six predictors and can be expressed as:
yit=b0+b1Z500+b2q850+b3AAOI+b4SSTKU+
b5SSTNA+b6SSTSP,
(1)
where i is number of stations, t is forecast time, b0, b1, b2,
b3, b4, b5, and b6 are constant and coefficients for predictors.
And the formula for the root mean square errors
(RMSE) is given by:
T
RMSE 
 y
t
 y 0t 2
t 1
,
(2)
N
where yt is hindcasts of models or downscaling; y0t is observation data; N and T present total numbers of stations
in the southeastern China region and forecast time length,
respectively.
3
3.1
Results
Model evaluation
Table 3 presents the model evaluation results. The CCs
are calculated between the ERA-40 reanalysis data and
the DEMETER data. For models CERFACS, CNRM,
INGV, and UKMO, Z500 and q850 revealed convincingly
high CCs (from 0.42 to 0.65, exceeding 95% significance
level). One predictor’s CCs reached 90% significance
level for models ECMWF and LODYC, while only MPI
exhibited low CCs at 0.29 and 0.15. As seen from Table 3,
most models produced satisfactory results. Hence, Z500
and q850 out of the seven individual models would be con-
175
sidered in the MLR scheme.
3.2 Relationship of rainfall anomaly between
observation and models or downscaling approach
To examine the efficiency of the downscaling framework in predicting JJA precipitation in southeastern China,
we calculated the anomaly correlation coefficients (ACCs)
between the observed and model-predicted or MLR
downscaling predicted JJA precipitation (Fig. 1), in which
the left and right represent before and after the downscaling scheme, respectively.
As Fig. 1 demonstrates, most of the models have no
prediction skills for summer rainfall over southeastern
China, and ACCs of most parts of southeastern China
were negative for the models CERFACS, INGV, LODYC,
MPI, and UKMO, with a minimum and maximum value
of 0.6 and 0.2, respectively. The significantly positive
value for models CNRM and ECMWF could be as high
as 0.42, but the areas were very small. The MME also
shows some prediction skill for the northwestern area.
After performing the statistical downscaling scheme,
ACCs over southeastern China were increased significantly for all the models, and the MME as ACCs in each
model and the MME become positive for most stations
(above 95% significance level), especially for the central
and northeastern parts (Fig. 1., the right column). Models
INGV, MPI, UKMO, and MME, have the areas in which
the maximum ACCs reached 0.8, much larger than the
99% significance level. Meanwhile, for CERFACS,
CNRM, LODYC, and MME, their values increased from
0.2 (see Fig. 1, the left column) to 0.42 (significant at
95% level) in the northeastern part of the entire region.
Additionally, as the results of ECMWF and INGV indicate, the ACCs achieved a value of 0.42 as compared to
0.4 in the central part of southeastern China.
3.3
Rainfall RMSE of before and after downscaling
The bias between the observation and models’ prediction before and after statistical downscaling was measured
by RMSE, with large RMSE differences between before
and after downscaling, indicating better prediction skills.
In this study, RMSE difference signifies that the RMSE of
the model distracted the RMSE of the downscaling
scheme. Fig. 2 presents the spatial patterns of RMSE for
precipitation anomaly differences between before and
after downscaling. As compared to the models’ prediction,
the downscaling scheme improved the predicted skill in
most parts of the northern region over southeastern China
for all the models and MME, and the large differences
were centralized in the northeastern area, indicating im-
Table 3 Correlation coefficients between regionally averaged predictors from seven DEMETER models, the MME, and ERA-40s for the 22-year
period (19802001).
CERFACS
CNRM
ECMWF
INGV
LODYC
MPI
UKMO
MME
Z500
0.65
0.45
0.76
0.45
0.30
0.29
0.64
0.71
q850
0.48
0.60
0.27
0.48
0.35
0.15
0.42
0.51
The underlined CCs indicate above 90% significance level.
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ATMOSPHERIC AND OCEANIC SCIENCE LETTERS
VOL. 4
Figure 1 ACCs between the observed and models-hindcast JJA precipitation for the 22-year period (19802001). The left column represent the
before downscaling scheme, and the right column represent the after downscaling scheme. Areas exceeding 95% significance level are shaded.
NO. 3
LIU ET AL.: STATISTICAL DOWNSCALING, SUMMER PRECIPITATION, SOUTHEASTERN CHINA
177
Figure 1 (Continued)
proved prediction skills. From the aforementioned analysis, these typical spatial distributions are similar to the
ACCs (Fig. 1), namely that stations in the northeastern
part were responding better to the predictors. Models
LODYC and UKMO presented improved results as
compared to the other models. Although Z500 and q850’s
predicted skill for the LODYC were not high (Table 3),
the downscaling result was superior, suggesting the role
of preceding SST factors on the JJA precipitation in the
key regions over southeastern China is very important.
Meanwhile, about one-quarter of the stations increased
the predicted level in most models and the MME. However, UKMO emerged as the best performer, and its
RMSE at more than half the stations (33 stations) was
larger than zero. However, ECMWF and INGV were the
worst performers, with larger than zero RMSE values
recorded at no more than 12 and 13 stations, respectively
(Table 4).
3.4 Temporal variations of the regionally averaged
JJA precipitation anomaly
The above results exhibit the station-to-station MLR
scheme’ predicted capability of JJA precipitation anomaly for the period of 19802001. In addition to these, we
evaluated the scheme performance on an average over
the entire region. For the observation data prior to 2000,
the trend of JJA precipitation anomaly over the southeastern China was rising with a subsequent decrease after
2000 (see Fig. 3). All of the models’ outputs failed to
capture the main variation in the tendency and the
year-to-year variability. In contrast, the downscaling
method was able to successfully record the entire increasing trend closer to the observations, particularly
CERFACS, CNRM, UKMO, and MME. The correlation
coefficients between the temporal variation of observation and the downscaling varies from 0.52 to 0.69, with
corresponding values of 0.27 to 0.22 for the observation
and the original output of models (CCs are not shown;
refer to Fig. 3). However, for 1991, 1994, 1998, and
2001, the downscaling method underestimated or overestimated the precipitation value. This was unavoidable, as
the cross-validation downscaling model can slightly reproduce extreme precipitation events.
All of these analyses suggest that the six predictors
applied to the downscaling scheme are appropriate for
predicting JJA precipitation anomaly over southeastern
China, especially in the central and northeastern parts.
Therefore, it might be possible to increase JJA precipitation anomaly prediction skills for individual models including the MME, by adopting this downscaling scheme.
4
Conclusions and discussions
In this study, statistical downscaling based on the
DEMETER multimodel outputs was used to predict JJA
rainfall anomaly over southeastern China. In particular,
Table 4 Numbers of stations for RMSE differences between before and after downscaling above zero.
CERFACS
CNRM
ECMWF
INGV
LODYC
MPI
UKMO
MME
Total
20
17
12
13
21
14
33
19
d≥1.0
1
0
0
1
2
2
2
1
0.5≤d<1.0
3
3
3
3
2
1
9
4
0≤d<0.5
16
14
9
9
17
11
22
14
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ATMOSPHERIC AND OCEANIC SCIENCE LETTERS
VOL. 4
Figure 2 Spatial distribution of JJA precipitation anomaly’s RMSE differences between before and after downscaling. The solid and hollow circles
denote the positive and negative differences, respectively. Units: mm d1.
Z500 and q850 from seven different global models were
used as predictors. Downscaling has been shown to considerably outperform global models in predicting regional
precipitation. To search for appropriate predictors in an
optimal window, we calculated the CCs between the observed JJA precipitation anomaly series over southeastern
China and the predictors globally. These six predictors not
only included preceding factors (SST and AAOI), but also
current summer variables (Z500 and q850). On the basis of
these six predictors in an optimal window, the MLR
downscaling scheme was found to adopt the leaving-oneyear-out cross-validation method, and subsequently, we
used this fitting downscaling scheme for every station to
predict the precipitation anomaly over southeastern China
for each year.
Examinations of the downscaling scheme using the
DEMETER multi-model hindcast datasets were performed. The results indicate that the downscaling scheme
can substantially improve JJA precipitation anomaly predictions in southeastern China. ACCs, RMSE, and regionally averaged precipitation temporal series predicted
by the downscaling scheme were carried out for comparisons with the models’ outputs.
All seven models and the MME revealed increased
ACCs between the downscaling predictions and the observations during 19802001, especially for the central
and northeastern regions of southeastern China. Differences between the models’ predictions and the downscaling scheme results showed that the RMSE decreased
mainly in the central and northeastern parts, similar to
ACCs distribution. Specifically, the downscaling framework is preferable for stations located in the central and
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LIU ET AL.: STATISTICAL DOWNSCALING, SUMMER PRECIPITATION, SOUTHEASTERN CHINA
179
Figure 3 Interannual variation of regionally averaged JJA precipitation anomaly during 19802001. A short dash line with a hollow circle indicates
the observation result, while the solid line with a solid dot and a solid line with a solid triangle represent the value of the model and the downscaling
scheme, respectively. Units: mm d1.
northeastern of southeastern China. The downscaling operation can reproduce the linear trend of regionally averaged JJA precipitation anomaly temporal variation, with
the exception of missing some extreme precipitation year.
On the whole, the RMSE exhibited decreased values for
all the seven models. Therefore, statistical downscaling
can be a powerful method for improving JJA precipitation
anomaly prediction over southeastern China effectively.
Due to the imperfect models’ outputs of Z500 and q850
and the limitation of the dataset’s time scale, the downscaling performances was not quite acceptable for some
of the models. In future, the downscaling scheme may be
improved by using long model outputs and more predictors.
Acknowledgements. This research is jointly supported by the
special Fund for Public Welfare Industry (Meteorology) (Grant No.
GYHY200906018), the National Basic Research Program of China
(Grant Nos. 2010CB950304 and 2009CB421406), and the Knowledge Innovation Program of the Chinese Academy of Sciences
(Grant No. KZCX2-YW-QN202).
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