91

Long-term Changes in Japanese Extreme Precipitation Analyzed with
APHRO_JP_EX
Kenji KAMIGUCHI*, Osamu ARAKAWA and Akio KITOH
Meteorological Research Institute
1-1 Nagamine, Tsukuba, Ibaraki 305-0052, Japan*e-mail: kkamigu@mri-jma.go.jp

Abstract
The authors constructed a dataset of historical extreme precipitation indices in Japan named
APHRO_JP_EX. This dataset contains more than 40 annual indices derived from daily precipitation.
Although the spatial coverage is limited to the land of Japan, the uniqueness of these data with their long
period (1900-2009) and high resolution (0.05 x 0.05 degrees) is useful not only for climate research but also
for application to studies such as risk assessment of hydrological disasters. Using this dataset, we analyzed
trends and annual variations of these indices to quantify the effects of global warming on local extreme
precipitation in Japan.
The number of wet days has significantly decreased all over Japan, especially in the northeastern part,
where about 20 wet days per year have been lost during the past century. The annual mean precipitation has
slightly decreased in most regions. Looking at the regional average of all of Japan, increased heavy
precipitation intensity is distinct. The local changes in heavy precipitation are larger in the western part; however, they are not significant in many areas due to large annual variations. When the former and the latter
halves of the 20th century are compared, increases in the annual variations of heavy precipitation indices are
remarkable in the latter period. While indices of wet spells and dry spells show clear regionality in their trends,
wet-spell indices have been decreasing on the Japan Sea side and dry-spell indices have been increasing on the
Pacific Ocean side, though both results indicate a progression of meteorological dryness.
After the beginning of the 20th century, in which surface temperatures were rising, precipitation in Japan
has become more extreme overall; however, we found regional differences in these signals.
Key words: climate model, extreme precipitation, global warming, IPCC, water resources

1. Introduction
As referred to in the Fourth Assessment Report of the
IPCC (Solomon et al., 2007), many regions around theworld are projected to see increased extremes in precipitation such as heavy rain or severe droughts in the
future due to global warming. The relationship between
global warming and precipitation changes has been
investigated in theoretical studies, as well as empiricalresearch which uses model simulations and observationdata.
As temperatures rise, the total amount of water in theatmosphere is indicated to increase at a rate of 7%/K byboth climate models and satellite observations, consistent
with the theoretical expectations of the ClausiusClapeyron equation (e.g., Wentz & Schabel, 2000;
Trenberth et al., 2005; Santer et al., 2007). However, thechanges in global precipitation are much slower, at a rateof 1 to 3%/K, according to many climate model experiments (Allen & Ingram, 2002; Wentz et al., 2007).
Global Environmental Research
15/2011: 91-99
printed in Japan

Weakening of atmospheric circulation is considered areason for this inconsistency between the rates of changeof atmospheric water vapor and precipitation amounts.
Held and Soden (2006) examined some aspects of thechanges in the hydrological cycle using CMIP3 (phase 3of the Coupled Model Intercomparison Project) Global
Circulation Models (GCMs), whose results indicated adecrease in convective mass fluxes, an increase inhorizontal moisture transport, an associated enhancementof the pattern of evaporation minus precipitation and its
temporal variance, and a decrease in the horizontal
sensible heat transport in the extratropics. Other studies
support the result that global warming weakens global
circulation such as Walker circulation and Hadleycirculation (Tanaka et al., 2005; Vecchi et al., 2006). As
for extreme precipitation studies, Allan and Soden (2008)
confirmed that observations reveal a distinct link
between rainfall extremes and temperature, with heavy
rain events increasing during warm periods and decreasing during cold periods. Furthermore, the observed©2011 AIRIES

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K. KAMIGUCHI et al.

amplification of rainfall extremes is found to be larger
than that predicted by models, implying that projectionsof future changes in extreme precipitation in response toanthropogenic global warming may be underestimated.
Historical and future changes in Japanese precipitation have also been investigated. A detailed analysis ofhistorical daily precipitation changes was done by Fujibe
et al. (2006) using 51 rain-gauge data from 1901 to 2004,
and they found heavy precipitation increases and wet
day decreases during the period. Mizuta et al. (2005)
projected future changes in extreme precipitation
indices under the IPCC SRES A1B emission scenario(Nakicenovic & Swart, 2000), using a 20-km-meshatmospheric general circulation model (AGCM). The
results indicated that annual precipitation would significantly increase in the western part of Japan and increasesof heavy precipitation would also be seen in almost thesame region.
Although, the study of precipitation changes has been
progressed as referred to above, the effect of global
warming on precipitation, especially on local extreme
precipitation, is not sufficiently understood, because of
the insufficiency of both observation and model data withhigh spatial resolution over a long term. A better understanding of that is crucial not only for scientific concerns
but also for application such as in water management
policy, agriculture plans, economic policy, and so on.
Consequently, this study investigated historical changes
in Japanese extreme precipitation spanning more than acentury, utilizing the new high-resolution daily precipitation data of APHRO_JP (Kamiguchi et al., 2010) to
quantify the global warming effect. “Extremeness”
should be measured from a multilateral viewpoint, notonly by magnitude but also by frequency or duration, so
this study used “extremes indices” which are used widelyin extreme precipitation analysis.

2. Data and Analysis Method
As a first step, we constructed APHRO_JP_EX(JP_EX), a suite dataset of extreme precipitation indicesin Japan. JP_EX comprises more than 40 indices of ex-treme precipitation, which are almost the same as those
proposed by STARDEX (STAtistical and Regional
dynamical Downscaling of EXtremes for the European
region; STARDEX, 2005). The advantage of using thisindex is that it makes it easy to compare results withthose of other similar studies, because it is widely used in
extreme precipitation analysis (e.g., Alexander et al.,2006; Kamiguchi et al., 2006; Endo et al., 2009). The
details of 17 indices used in this study are explained inTable 1. Each index is categorized into four groups aselaborated by Schmidli and Frei (2005). “Basic” indicesdiagnose mean states such as annual mean precipitation
amounts and the numbers of wet days, i.e., days with
precipitation of at least 1 mm/day, while “heavy precipitation” indices are used for analysis of the frequency
or intensity of heavy precipitation. Both “wet-spell” and“dry-spell” indices analyze the frequency or persistenceof meteorological conditions for wetness and dryness,
respectively. All indices were calculated each year using
daily precipitation data provided by APHRO_JP(Kamiguchi et al., 2010). APHRO_JP is a historical(1900-2009) and high-resolution (0.05 x 0.05 degree)terrestrial daily precipitation data, created using rain
gauge observations and elaborate interpolation techniques, considering local topographical features andstatistical characteristics of precipitation. Thereby theestimation bias of grid values remained relatively smalleven in early years of the 20th century, in which rain
gauges were few in number. The number of rain gauges
used in APHRO_JP was less than 100 until the 1940s, but
after the late 1970s, it increased to more than 1,300 due tothe installation of AMeDAS (Automated Meteorological
Data Acquisition System). AMeDAS is a high-density
surface observation network system developed by theJapan Meteorological Agency (JMA) which consists ofautomatic observation equipment and a telecommunications system. The temporal changes in spatial rain gauge
density may cause an artificial trend in the long-term
analysis of extreme precipitation, as suggested by
Kamiguchi et al. (2010). For such purposes, APHRO_JP
provides optional data produced by these identical rain
gauges constantly available throughout the period. Using

Table 1 Definitions of 17 extremes indices used in the analysis.

mm
mm
mm
mm
day
day
day
day

basic

kind

heavy
precipitation

unit
mm/dayday
mm/daymm/daymm/dayday

wetspellsdefinition
Annual mean of daily precipitation
Number of wet_days (>=1mm/day)
Simple daily intensity. Mean precipitation intensity on wet_days
Daily precipitation intensity at 90th percentile
Daily precipitation intensity at 95th percentile
Number of days with heavy precipitation (>=10mm/day)Fraction of total precipitation above 95th percentile of wet_day amount
Annual maximum daily precipitation totals
Annual maximum of 3-day precipitation totals
Annual maximum of 5-day precipitation totals
Annual maximum of 10-day precipitation totals
Mean wet_day (>=1mm/day) persistence
Annual maximum of consecutive wet days (>=1mm/day)
Mean wet spell length
Mean dry_day (>=1mm/day) persistence
Annual maximum of consecutive wet days (>=1mm/day)
Annual maximum of consecutive dry days (<1mm/day)dryspells

index
PAV
R1
SDII
PQ90
PQ95
R10
PF95
R1D
R3D
R5D
R10D
PWW
CWD
MWS
PDD
CDD
MDS

Long-term Changes in Japanese Extreme Precipitation Analyzed with APHRO_JP_EX

93

this data, we also constructed APHRO_JP_EX_CSTN(EX_CSTN). Long-term changes in precipitation wereanalyzed mainly using EX_CSTN. The specifications of
JP_EX and EX_CSTN are almost the same as that of
APHRO_JP. The spatial coverage includes the wholeland of Japan (123°E-146°E, 24°N-46°N) with anapproximately 5-km (0.05 x 0.05 degree) grid, and thetemporal coverage is the 110 years from 1900 to 2009.
JP_EX was produced with an eye on HadEX(Alexander et al., 2006), which is also a suite of climatechange indices derived from daily temperature andprecipitation with a primary focus on extreme events.
HadEX includes seasonal and annual extreme indices forthe period of 1951-2003, with 16 and 11 extreme indicesfor temperature and precipitation. Although HadEX covers almost all global land areas, it is not suitable forregional analysis because of its coarse horizontal resolution (2.5 x 3.75 degrees). Both JP_EX and EX_CSTN are
freely available from the web site <http://www.chikyu.
ac.jp/precip/> for academic research purposes. The
analysis covered the entire land of Japan, with the
exception of some surrounding territories (Fig. 1).

3. Results

Fig. 1 The study area and locations of the 47 prefecturesof Japan. The digits in the figure indicate theprefectural number. (see Table 2)

Figure 2 shows the geographical distribution of theclimatology of eight extreme precipitation indices. HereFig. 2 Spatial distribution of extremes indices calculated with JP_EX and CSTN_EX. The values are 30-year averages
between 1980 and 2009. (see Table 1)

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K. KAMIGUCHI et al.

we have defined “climatology” as the 30-year average
from 1980-2009, because the rain-gauge density used in
JP_EX is extremely high and has remained almost constant during the period due to the installation of
AMeDAS prior to that. To determine the effects of rain
gauge density on extreme indices, results by both JP_EX
and CSTN_EX are displayed. The spatial densities of therain gauges used in the former and the latter correspondto one station per 17 km and 80 km, respectively.
Although CSTN_EX has a horizontal resolution of 5 km,
it is unclear whether the index value has a spatial
representation of such a resolution, considering its lower
rain gauge density. Comparing JP_EX and CSTN_EX,
no large difference between them is recognized, whichindicates sufficient quality of CSTN_EX for analysis ofregional extreme precipitation. Consequently, we use
only CSTN_EX hereafter in our long term analysis.
The apparent regionality of each index is shown in
Fig. 2. PAV, the annual mean precipitation, shows large
values in two regions, the northern part of Japan, facing
the Japan Sea, and the southwestern part facing the

Pacific Ocean. The former region has much snowfall in
winter while the latter has much rainfall during summer
caused by the Baiu front and typhoons. The Baiu front isa typical stationary frontal system producing rainfall inearly summer in Japan accompanied with high humid
low level jets (Pham et al., 2008). Both regions havealmost the same value of PAV, but the precipitation characteristics differ considerably. The former region has
many wet-days (R1) with weak precipitation while thelatter has small R1 and a large value of heavy precipitation indices (e.g., PQ95 and R1D). CWD and CDD also
have an apparent regionality, taking large values on both
the Japan Sea and the Pacific Ocean side.
Figure 3 shows long-term changes in this anomaly
from the viewpoint of surface temperature climatology
and eight extremes indices. The temperature anomaly
data are nearly the same as those published by JMA,
except for the period of climatology, which is a mean of
17 surface observatories in Japan less affected byurbanization. The anomaly of extreme precipitation
indices is defined as the ratio to the climatology at gridFig. 3 Anomalies of surface temperature and extremes indices. The thick lines are the graph of the 11-year
moving average. The figures are the correlation coefficient between the moving-averaged temperatureand extremes indices, and parentheses indicate the correlation is not significant at the 99 % confidence
level. (see Table 1)

Long-term Changes in Japanese Extreme Precipitation Analyzed with APHRO_JP_EX

cells, and its average in the study area is shown in the
figure. The climatology period is the 30 years from 1980
to 2009 for both temperature and extremes indices. The
correlation coefficient between the 11-year movingaverage temperature and extremes indices is also shownin the figure.
Surface temperatures have been almost consistently
rising since the beginning of the 20th century, though arelatively cold period was seen around the 1970s. PAV
underwent some remarkable changes (1923/1924, 1953,
1984), though the trend during the entire period is notclear. This result is consistent with a previous study doneby Xu et al. (2003), which examined monotonic trends
and step changes of spatially averaged annual precipitation at 46 rain gauges in Japan from 1897 to 1999, using aparametric t-test, and nonparametric Mann-Kendall andMann-Whitney techniques. Their results indicated that
although several step changes occurred in Japaneseprecipitation, the time series did not exhibit significant
evidence of a monotonic trend during the past century.
The mechanism of an abrupt change around 1983 was
examined by Inoue and Matsumoto (2007) who explained that shifts in the positions and tracks of typhoons
were responsible for this change in precipitation. What ismost remarkable in the PAV time series is the large
interannual variation after the 1970s. The three lowest
PAV values were all recorded in this period (1984, 1994
and 1978). Considering that there is no significant
correlation between it and the surface temperature, PAV
seems to be more affected by natural variability than by
global warming. The sudden shifts shown in PAV are
called regime shifts (Minobe, 1997), and are suggested tobe related to the Pacific decadal oscillation (e.g., Mantua
et al. 1997).
R1 is similar to PAV in annual variation, but its
downward trend is clearer than that of PAV. The value

95

changed greatly around 1960, and low values have beenfound after that. Although its negative correlation withtemperature is high (r = – 0.64), R1 has undergone no
major changes since the 1970s, while surface
temperature has been continuously rising. SDII and all
heavy precipitation indices (PQ95, R1D and R5D) havelarge annual variations. However, an upward trend isvery clear and almost monotonic without step changes,
unlike those of PAV and R1. Changes in heavy precipitation have also been reported by Fujibe et al. (2006),
who noted that the frequency of Japanese heavy precipitation in the upper 10% of their definition increasedby 2.3% per decade during the 20th century. Positive
correlation with temperature is also high especially for
SDII (r = 0.87) and PQ95 (r = 0.88), implying that heavy
precipitation might be more affected by global warming
than by natural variability. CWD and CDD have almost
the same and opposite time series, respectively, to R1.
The major changes are found around 1960, as seen for R1,
indicating progression of meteorological dryness inrecent years. A high positive correlation with surface
temperature has also been shown for CWD (r = 0.71).
In order to investigate historical changes in annual
variations, we looked into the statistical distribution of
extremes indices. The total period was separated into
three equal sub-periods of 36 years (1900-1935, 19371972 and 1964-2009; hereafter, referred to as FP, MP andLP, respectively) and the index value at the percentiles of90, 75, 50 (median), 25 and 10 were compared in eachperiod. For example, the 90th percentile means the top
quarter of values during the 36 years. The percentile
value at the grid box was normalized by dividing it by thegrid climatology, and then the normalized values wereaveraged throughout Japan.
Figure 4 shows box-whisker graphs of the 17 indicesin the three periods. The median of PAV and R1 are

Fig. 4 Box-whisker plot of 17 extremes indices in three periods: the former period (FP: 1900-1935),
the middle period (MP: 1937-1972) and the latter period (LP: 1974-2009).

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K. KAMIGUCHI et al.

clearly smaller in the LP period than in the other periods.
For R1 in particular, the median in LP was almost thesame as the 10th percentile in MP which means that rare
events occurred once per ten years in MP, but once per
two years in LP. Indices of heavy daily precipitation,
PQ90 and PQ95, increased in LP, while R10, by contrast,
decreased. The number of days with heavy precipitation
exceeding 50mm/day (R50) increased in LP, but the number of days with precipitation intensity of less than
40 mm/day decreased in Japan (not shown). Changes in
distribution width (e.g. 75 percentile – 25 percentile) are
noticeable in indices of the annual maximum n-day precipitation (R1D, R3D, R5D and R10D), although thechanges in the median are not so large. The extension of
the upper distribution tail found in these indices may
have a higher impact, especially on hydrological disasters, than the increase in the median. On the other hand,

obvious changes in the median are recognized in wet
spell and dry spell indices, although the distribution
width did not change so much. The progression of meteorological dryness in LP is evident from the figure.
Finally, the linear trends of eight indices during
1900-2009 were calculated in each prefecture to quantify
the effect of global warming on local extreme precipitation in Japan. The calculation was done for each of the
47 prefectures, whose names and locations are listed inTable 2 and Fig. 1. Normalization by climatology as donein the previous analyses was not applied because precipitation characteristics are not so diverse over small prefectural areas. As shown in Fig. 5 and Table 3, PAV has
shown a downward trend in almost all prefectures, but
not many of these are significant. Large decreases are
remarkable in western Japan on the Pacific Ocean side.
Decreases in R1 are statistically significant in all prefec-

Table 2 Prefecture number, name and code. Locations are shown in Fig. 1. All Japan includes all 47 prefectures.
no

name

code

no

1 Hokkaido

HKD

2 Aomori

AOM

3 IwateIWT

4 Miyagi

MYG

name

code

no

13 Tokyo

TKY

14 Kanagawa

KNG

15 Niigata

NGT

16 Toyama

TYM

name

code

no

name

code

25 Shiga

SIG

37 Kagawa

KGW

26 Kyoto

KYT

38 Ehime

EHM

27 OsakaOSK

39 KochiKOC

28 Hyogo

HYG

40 Fukuoka

FKO

5 AkitaAKT

17 Ishikawa

ISK

29 NaraNAR

41 SagaSAG

6 Yamagata

YGT

18 Fukui

FKI

30 Wakayama

WKY

42 Nagasaki

NGS

7 Fukushima

FKS

19 Yamanashi

YMN

31 Tottori

TTR

43 Kumamoto

KMM

8 Ibaraki

IBR

20 Nagano

NGN

32 Shimane

SMN

44 Oita

OIT

9 Tochigi

TCG

21 GifuGIF

33 Okayama

OKY

45 Miyazaki

MYZ

10 Gunma

GNM

22 Shizuoka

SZO

34 Hiroshima

HRS

46 Kagoshima

KGS

11 Saitama

SIT

23 AichiAIC

35 Yamaguchi

YGC

47 Okinawa

OKN

12 Chiba

CHB

24 MieMIE

36 Tokushima

TKS

48 All Japan

JPN

Fig. 5 Linear trends of extremes indices in the period of 1900-2009. The values are the rate of change per 100 years. They were calculatedusing the mean value in each prefecture (see Table 2 and Fig. 1). Hatched areas indicate that the trend is not statistically significant at
the 95% level.

Long-term Changes in Japanese Extreme Precipitation Analyzed with APHRO_JP_EX

97

Table 3 The climatology of extremes indices (means of 1980-2009) and its linear trend per 100 years in each region (47 prefectures and allof Japan). Bold style indicates that the trend is statistically significant at the level of 95%.

98

K. KAMIGUCHI et al.

tures and the rate of decrease is larger in Tohoku in thenortheastern part of Japan. If we look at the averagethroughout Japan, we can see that Japan has lost 16.8 wetdays per year during these past 100 years. Kimoto et al.
(2005) projected a decrease in R1 in Japan of 8.5 days
during the 100 years between the end of 20th century andthe end of 21st century, using a high-resolution coupledgeneral circulation model (CGCM). SDII and otherheavy precipitation indices have shown an upward trend,
especially in the western part of Japan, including Kyushu
and Okinawa, in which the climatology is large. A modelexperiment study by Mizuta et al. (2005) using a 20-km
mesh AGCM projected an increase in heavy precipitationin the western part of Japan at the end of the 21st century.
Our results are almost consistent with theirs. CWD and
CDD show clear regionality in their trends: the former
has decreased on the Japan Sea side and the latter has
increased on the Pacific Ocean side. The magnitude of
the trend is large in the regions in which the climatology
is large. The decrease in winter snowfall on the Japan Sea
side greatly affects the reduction of CWD in that region.

4. Discussion
Future projection of extreme weather events is being
vigorously promoted in Japan. In the KAKUSHIN
program (Innovative Program of Climate Change
Projection for the 21st Century), a 20-km-mesh AGCM
and 5-km-mesh cloud-resolving regional model havebeen developed (Kitoh, 2009). Using these models,
future changes in tropical cyclones and extreme
precipitation are being investigated. The KAKUSHIN
program also focuses on application studies such as
regional projection for natural disaster prevention. For
example, Takara et al. (2009) examined the potential
impact of climate change on Tokyo metropolitan water
resources in the Tone River basin (16,840 km2) using a
20-km-mesh AGCM. The results indicated that the
yearly standardized precipitation index (SPI) predicts
more frequent dry conditions, indicating increased future
vulnerability to subtle droughts.
However, model studies always have the problem of
uncertainty in their predictions. To cope with this,elucidation of past changes and model evaluation using
observation data are essential. Japan fortunately has hadhigh quality rain gauge data since the late 19th century
owing to the great efforts of our foregoers. Utilizing thederivatives of such data, we produced APHRO_JP_EX, asuite dataset of extreme precipitation indices, andanalyzed their long-term changes to quantify the effects
of global warming on local extreme precipitation in
Japan.
In this study, we found that precipitation in Japan has
become more extreme overall since the beginning of the20th century, with surface temperatures rising, as isconsistent with previous studies such as Fujibe et al.
(2006). In addition, we could newly reveal regional
differences in historical changes owing to the high spatial
resolution of APHRO_JP_EX. Our results are easy tocompare with those of other similar studies because theextremes indices used here are widely used in bothobservation and model data. For example, using HadEX,
Alexander et al. (2006) showed that SDII in Japan has
had a negative trend during the latter part of the 20th
century, which is the opposite of our result. Although thereason for such a discrepancy is unclear, the quality andnumber of rain gauge data used in APHRO_JP_EX aresuperior to those in HadEX.
This study analyzed only annual indices of extreme
precipitation. However, because Japanese precipitation
has a wide variety of features in different areas anddifferent seasons, the climate mechanisms provoking the
local changes in extreme precipitation should be further
examined. We were also unable to divide the effects ofglobal warming from those of natural variability on
extreme precipitation sufficiently in this study. Those
will be targets of further research.

Acknowledgements
This study was supported by the Environment
Research & Technology Development Fund (A0601),
Ministry of the Environment of Japan.
References
Alexander, L. V., X. Zhang, T. C. Peterson, J. Caesar, B. Gleason,
A. M. G. Klein Tank, M. Haylock, D. Collins, B. Trewin, F.
Rahimzadeh, A. Tagipour, K. Rupa Kumar, J. Revadekar, G.
Griffiths, L. Vincent, D. B. Stephenson, J. Burn, E. Aguilar, M.
Brunet, M. Taylor, M. New, P. Zhai, M. Rusticucci and J. L.
Vazquez-Aguirre (2006) Global observed changes in daily
climate extremes of temperature and precipitation. Journal ofGeophysical Research, 111: D05109.
Allan, R. P. and B. J. Soden (2008) Atmospheric Warming and the
Amplification of Precipitation Extremes. Science, 321: 14811484.
Allen, M. R. and W. J. Ingram (2002) Constraints on futurechanges in climate and the hydrologic cycle. Nature, 419:
224-32.
Endo, N., J. Matsumoto and T. Lwin (2009) Trends in Precipitation
Extremes over Southeast Asia. SOLA, 5: 168-171.
Fujibe, F., N. Yamazaki and K. Kobayashi (2006) Long-Term
Changes of Heavy Precipitation and Dry Weather in Japan(1901-2004). Journal of the Meteorological Society of Japan, 84(6): 1033-1046.
Held, I. M. and B. J. Soden (2006) Robust Responses of theHydrological Cycle to Global Warming. Journal of Climate,19(21): 5686-5699.
Inoue, T. and J. Matsumoto (2007) Abrupt Climate Changes
Observed in Late August over Central Japan between 1983 and1984. Journal of Climate, 20: 4957-4967.
Kamiguchi, K., A. Kitoh, T. Uchiyama, R. Mizuta and A. Noda
(2006) Changes in Precipitation-based Extremes Indices Due to
Global Warming Projected by a Global 20-km-mesh Atmospheric Model. SOLA, 2: 64-67.
Kamiguchi, K., O. Arakawa, A. Kitoh, A. Yatagai, A. Hamada andN. Yasutomi (2010) Development of APHRO_JP, the firstJapanese high-resolution daily precipitation product for morethan 100 years. Hydrological Research Letters, 4: 60-64.
Kimoto, M., N. Yasutomi, C. Yokoyama and S. Emori (2005)
Projected Changes in Precipitation Characteristics around Japan.
SOLA, 1: 85-88.

Long-term Changes in Japanese Extreme Precipitation Analyzed with APHRO_JP_EX
Kitoh, A., T. Ose, K. Kurihara, S. Kusunoki, M. Sugi and
KAKUSHIN Team-3 Model Group (2009) Projection ofchanges in future weather extremes using super-high-resolution
global and regional atmospheric models in the KAKUSHIN
Program: Results of preliminary experiments. HydrologicalResearch Letters, 3: 49-53.
Mantua, N. J., S. R. Hare, Y. Zhang, J. M. Wallace and R. C.
Francis (1997) A Pacific Interdecadal Climate Oscillation with
Impacts on Salmon Production. Bulletin of the American
Meteorological Society, 78(6): 1069-1079.
Minobe, S. (1997) A 50-70 year climatic oscillation over the North
Pacific and North America. Geophysical Research Letters,
24(6): 683-686.
Mizuta, R., T. Uchiyama, K. Kamiguchi, A. Kitoh and A. Noda
(2005) Changes in Extremes Indices over Japan Due to Global
Warming Projected by a Global 20-km-mesh Atmospheric
Model. SOLA, 1: 153-156.
Nakicenovic, N. and R. Swart, eds. (2000) Emissions Scenarios,
Special Report of the Intergovernmental Panel on Climate
Change, Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA.
Pham, N. T., K. Nakamura, F. A. Furuzawa and S. Satoh (2008)
Characteristics of Low Level Jets over Okinawa in the Baiu andpost-Baiu Seasons Revealed by Wind Profiler Observations.
Journal of the Meteorological Society of Japan, 86: 699-717.
Santer, B. D., C. Mears, F. J. Wentz, K. E. Taylor, P. J. Gleckler,
T.M. L. Wigley, T. P. Barnett, J. S. Boyle, W. Bruggemann, N. P.
Gillett, S. A. Klein, G. A. Meehl, T. Nozawa, D.W. Pierce, P. A.
Stott, W.M. Washington, and M. F. Wehner (2007) Identificationof human-induced changes in atmospheric moisture content.
Proceedings of the National Academy of Sciences of the UnitedStates of America, 104(39): 15248-15253.
Schmidli, J. and C. Frei (2005) Trends of heavy precipitation andwet and dry spells in Switzerland during the 20th century.
International Journal of Climatology, 25(6): 753-771.
Solomon, S., D. Qin, M. Manning, Z. Chen, M. Marquis, K.B.
Averyt, M. Tignor and H.L. Miller, eds. (2007) Contribution ofWorking Group I to the Fourth Assessment Report of theIntergovernmental Panel on Climate Change, Cambridge
University Press, Cambridge, United Kingdom and New York,NY, USA.
STARDEX (2005) The STARDEX Final Report-Downscaling
Climate Extremes, http://www.cru.uea.ac.uk/cru/projects/stard
ex/ reports/STARDEX_FINAL_REPORT.pdf
Takara, K., S. Kim, Y. Tachikawa and E. Nakakita (2009)
Assessing Climate Change Impact on Water Resources in theTone River Basin, Japan, Using Super-High-Resolution. Journalof Disaster Research, 4(1): 12-23.
Tanaka, H. L., N. Ishizaki and D. Nohara (2005) Intercomparisonof the Intensities and Trends of Hadley, Walker and Monsoon
Circulations. SOLA, 1: 77-80.
Trenberth, K. E., J. Fasullo and L. Smith (2005) Trends andvariability in column-integrated atmospheric water vapor.
Climate Dynamics, 24: 741-758.
Vecchi, G. a, B. J. Soden, A. T. Wittenberg, I. M. Held, A. Leetmaa
and M. J. Harrison (2006) Weakening of tropical Pacificatmospheric circulation due to anthropogenic forcing. Nature,
441: 73-76.

99

Wentz, F. and M. Schabel (2000) Precise climate monitoring using
complementary satellite data sets. Nature, 403: 414-416.
Wentz, F. J., L. Ricciardulli, K. Hilburn and C. Mears (2007) How
much more rain will global warming bring? Science, 317:
233-235.
Xu, Z. X., K. Takeuchi and H. Ishidaira (2003) Monotonic trend
and step changes in Japanese precipitation. Journal of
Hydrology, 279(1-4): 144-150.

Kenji KAMIGUCHI
Kenji Kamiguchi is a Senior Researcher of the
Climate Research Department at the Meteorological Research Institute (MRI). He graduatedfrom the Meteorological College in 1997, and thenworked at weather forecast section of the Japan
Meteorological Agency (JMA). Since moving to
MRI, he has worked mainly on development of statistical analysis for
precipitation. His favorite quote is “I have deep faith that the principal of
the universe will be beautiful and simple,” said by Albert Einstein.

Osamu ARAKAWA
Osamu Arakawa is a Research Associate at theMeteorological Research Institute (MRI) of theJapan Meteorological Agency (JMA). He isinterested in atmospheric science and climatology. He has used climate models to study climate
variations from the intra-seasonal to intra-decadal
timescales, mainly over the tropics.

Akio KITOH
Akio Kitoh is Director of the Climate Research
Department at the Meteorological ResearchInstitute (MRI), Japan Meteorological Agency(JMA). He has a Doctor of Science degree from
Kyoto University. He is leading two projects,“Research on Prediction of Climate andEnvironmental Change to Contribute to Mitigation Plan Decision Making
Against Climate Change,” funded by the Japan Meteorological Agency(JMA) (FY2010-2014) and “Projection of Change in Future Weather
Extremes Using Super-High Resolution Atmospheric Models,” funded by
the Ministry of Education, Culture, Sports, Science and Technology(MEXT) (FY2007-2011). He was lead author of the IPCC Working Group I
Second, Third and Fourth Assessment Reports and is lead author of the
IPCC Working Group I Fifth Assessment Report.

(Received 15 July 2011, Accepted 7 October 2011)