Weather and Climate Extremes 11 (2016) 41–52

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Weather and Climate Extremes
journal homepage: www.elsevier.com/locate/wace

Changes in precipitation extremes projected by a 20-km mesh global
atmospheric model
Akio Kitoh a,n, Hirokazu Endo bab

University of Tsukuba, Tsukuba, Ibaraki 305-8572, Japan
Meteorological Research Institute, Tsukuba, Ibaraki 305-0052, Japan

art ic l e i nf o

a b s t r a c t

Article history:
Received 25 June 2015
Received in revised form
25 August 2015
Accepted 5 September 2015
Available online 14 September 2015

High-resolution modeling is necessary to project weather and climate extremes and their future changes
under global warming. A global high-resolution atmospheric general circulation model with grid size
about 20 km is able to reproduce climate ﬁelds as well as regional-scale phenomena such as monsoonal
rainfall, tropical and extratropical cyclones, and heavy precipitation. This 20-km mesh model is applied
to project future changes in weather and climate extremes at the end of the 21st century with four
different spatial patterns in sea surface temperature (SST) changes: one with the mean SST changes by
the 28 models of the Coupled Model Intercomparison Project Phase 5 (CMIP5) under the Representative
Concentration Pathways (RCP)-8.5 scenario, and the other three obtained from a cluster analysis, inwhich tropical SST anomalies derived from the 28 CMIP5 models were grouped. Here we focus on futurechanges in regional precipitation and its extremes. Various precipitation indices averaged over the
Twenty-two regional land domains are calculated. Heavy precipitation indices (maximum 5-day precipitation total and maximum 1-day precipitation total) increase in all regional domains, even wheremean precipitation decrease (Southern Africa, South Europe/Mediterranean, Central America). South Asia
is the domain of the largest extreme precipitation increase. In some domains, different SST patterns
result in large precipitation changes, possibly related to changes in large-scale circulations in the tropicalPaciﬁc.
& 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license(http://creativecommons.org/licenses/by/4.0/).

Keywords:
Heavy precipitation
Climate change
High-resolution
GCM

1. Introduction
Both too much water and too little water are of great concern
for human life because the contrasts in precipitation between wet
and dry regions and between wet and dry seasons are projected toincrease in a coming future world (Intergovernmental Panel on
Climate Change (IPCC), 2013). Warmer climate should theoreticallylead to more precipitation extremes due to increasing atmosphericwater vapor content (Allen and Ingram, 2002; Allan and Soden,2008). The intensity of precipitation extremes, however, depends
on not only water vapor content but also on atmospheric environmental changes (O'Gorman and Schneider, 2009). Based on
global climate model (GCM) simulations of the Coupled Model
Intercomparison Project Phase 3 (CMIP3, Meehl et al., 2007), it isassessed that the frequency of heavy precipitation or the proportion of total rainfall from heavy rainfalls will likely increase in the
21st century over many areas of the globe (IPCC, 2012). The samenCorrespondence to: Faculty of Life and Environmental Sciences, University ofTsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8572, Japan.
E-mail address: kito.akio.ff@u.tsukuba.ac.jp (A. Kitoh).

assessment was made with the CMIP5 (Taylor et al., 2012) models
under Representative Concentration Pathways (RCPs) such that
extreme precipitation events over most of the mid-latitude landmasses and over wet tropical regions will very likely become more
intense and more frequent by the end of this century, as global
mean surface temperature increases (IPCC, 2013).
The CMIP5 models have better performance than the previous
CMIP3 models in simulating precipitation extremes in the present
climate (Sillmann et al., 2013a). A part of this improvement may
come from increasing horizontal resolution, about 280 km in
CMIP3 versus about 200 km in CMIP5. Spatial distribution of
precipitation indices is reasonably reproduced but differences arestill found in precipitation intensity where the magnitude of precipitation extremes is underestimated by climate models (Sillmann et al., 2013a; Mehran et al., 2014).
In a future warming world, change rate of heavy precipitation
amounts will generally increase more than that of annual mean
precipitation (Tebaldi et al., 2006; Sun et al., 2007; Sillmann et al.,2013b). The increasing rate of annual mean precipitation at theend of the 21st century projected by CMIP5 models in RCP8.5
scenario is 9% (median value), while that in simple daily intensity

http://dx.doi.org/10.1016/j.wace.2015.09.001
2212-0947/& 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

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A. Kitoh, H. Endo / Weather and Climate Extremes 11 (2016) 41–52

index (SDII) deﬁned as annual total precipitation divided by the
number of wet days is 12% and that in annual maximum 5-day
precipitation total (R5d) is 20% (Sillmann et al., 2013b). This increasing rate depends on the scenario, where the change ratio of
R5d is 6% in RCP2.6 and 10% in RCP4.5, respectively. Similarly, 20year or 50-year return values of daily precipitation are projected toincrease and 20-year or 50-year return periods for the present
precipitation events will reduce in the future almost everywhere
except for subtropical dry regions (Kharin et al., 2013; Toreti et al.,2013). The 20-year return values increase about 6% per a degreechange in annual mean surface temperature, but with considerable inter-model variability (Kharin et al., 2013). Large variability isalso known among models in simulated increase of precipitation
extremes, particularly in summer when organized convection
matters (Toreti et al., 2013).
Both thermodynamical and dynamical factors are responsible
for regional precipitation changes, with the former dominating thelatter for extreme precipitation changes (Emori and Brown, 2005).
Increase in maximum atmospheric water vapor due to temperature rise is a principal factor for increasing the intensity of individual precipitation events (Kunkel et al., 2013). There are additional factors such as changes in updrafts, which controlschanges in moisture ﬂux convergence, and soil moisture over land.
Representation of precipitation, particularly its intensity, depends
on the horizontal resolution of models. Coarse horizontal resolution of GCMs tends to prevent simulating realistic extreme events(Sun et al., 2006; Min et al., 2011). High-resolution modeling or
statistical downscaling is then necessary to project weather andclimate extremes and their future changes under global warming.
Dynamical downscaling is able to simulate more realistic,
though not perfect, extreme events. Regional climate models(RCMs) would add values on more realistic topography and lower
boundary conditions and better representation of dynamical processes (e.g., Takayabu et al., 2015). Therefore RCMs are widely used
to project future changes in extremes. For example, Fowler et al.
(2007) used six RCMs integrations under the SRES A2 scenario todownscale extreme precipitation changes over Europe at the endof the 21st century. Horizontal resolution of these RCMs is about
50 km. They found that all RCMs project increases in magnitude ofextreme precipitation from 1-day to 10-day duration for most ofEurope. It is also found that the magnitude of change is inﬂuenced
by the driving GCMs but projected scatter is moderated by the
RCM probably due to better representation of extreme events in
RCM. RCMs also give different spatial pattern of precipitation extremes than that obtained by parent GCMs.
Another way for downscaling is to use an atmospheric generalcirculation model (AGCM) with horizontally high-resolution (Kitoh
et al., 2009, 2015). Higher resolution model is needed to better
reproduce precipitation climatology such as the East Asia summer
rain band (Kitoh and Kusunoki, 2008), over India (Rajendran et al.,2013), South America (Kitoh et al., 2011), Central America and the
Caribbean (Nakaegawa et al., 2014). Kamiguchi et al. (2006) analyzed future changes of precipitation indices between the present
climate and the future climate at the end of the 21st century underthe IPCC Special Report on Emission Scenario (SRES) A1B scenario
by two 10-year simulations with the global 20-km mesh AGCM
developed at the Meteorological Research Institute (MRI) version
3.1 (MRI-AGCM3.1, Mizuta et al., 2006). They found signiﬁcantly
increased heavy precipitation in South Asia, the Amazon and West
Africa. Over the Amazon, an increase in dry spell is found during
the dry season in the future, which is related to the Walker circulation changes associated with the El Niño-like SST changes in
the future.
The MRI-AGCM has been updated recently (MRI-AGCM3.2,
Mizuta et al., 2012), which improved heavy precipitation climatology around the tropical western Paciﬁc and the global

distribution of tropical cyclones (Murakami et al., 2012). In thispaper, after evaluating the present-day precipitation extremes of
20-km mesh MRI-AGCM3.2, we analyze projected future changes
in precipitation extremes at the end of the 21st century. The scenario is based on RCP8.5, and four different spatial patterns in sea
surface temperature (SST) changes are used as boundary conditions. This type of simulations, which uses the observed presentday interannually varying SST plus ensemble mean future SSTchanges obtained by CMIP-class models, can minimize the effects
of climate model bias (Kitoh et al., 2015). In particular, the 20-km
mesh MRI-AGCM has superiority in reproducing precipitation extremes in its present-day experiments; the results obtained in the
future climate projections can be used widely for impact studies
from future changes of extremes.

2. Model and experiment2.1. MRI AGCM
We used the MRI-AGCM3.2 (Mizuta et al., 2012), which is the
updated version from the MRI-AGCM3.1 (Mizuta et al., 2006). The
model is based on a hydrostatic primitive equation system using aspectral transform method of spherical harmonics. The 20-km
mesh version uses a triangular truncation at wave number 959(TL959) in the horizontal, which has 1920  960 grid points. There
are 64 layers in the vertical with a top at 0.01 hPa. A two-timelevel semi-implicit semi-Lagrangian scheme is employed for time
integration. For cumulus parameterization scheme, a new massﬂux type scheme (Yoshimura et al., 2015) is used. This Yoshimura
scheme is based on Tiedtke (1989) scheme with a detailed entraining and detraining plume within a single grid cell, but also
allows multiple convective updrafts with different heights to exist
as with the Arakawa-Schubert (1974) type scheme. Prognostic
variables include cloud water and cloud amount. The radiation
code considers absorptions by greenhouse gases in the long wave
scheme. The direct effect of aerosol (sulfate, black carbon, organiccarbon, mineral dust, and sea salt) is included, but indirect effects
are not considered in this experiment.
2.2. Experiment method
We have conducted the Atmospheric Model Intercomparison
Project (AMIP)-type simulations for the present climate (1979–2003) and the future climate at the end of the 21st century (2075–
2099). The present climate simulation used the observed interannually varying monthly mean SST and sea-ice concentration
during 1979–2003 based on the HadISST1.1 data (Rayner et al.,2003). For the future climate, the boundary SST data were prepared by superposing the future change in the multi-model ensemble of SST projected by CMIP5 multi-model dataset to thepresent-day observed SST. In short, the future SST is the sum of(i) the trend in the multi-model ensemble (MME) of SST projectedby CMIP5 multi-model dataset, (ii) future change in MME of SST
for 2075–2099 and (iii) de-trended observed SST for the period
1979–2003. See Mizuta et al. (2008, 2014) for the details. Fig. 1ashows the annual mean SST changes of the 28 CMIP5 models. It isnoted that there is a large contrast in SST warming between the
Northern Hemisphere and the Southern Hemisphere, with theformer warming more than the latter. In the tropics, relatively
larger warming is noted in the central and eastern equatorial Paciﬁc. Over the Indian Ocean, a warming in the western Indian
Ocean is larger than that in the eastern Indian Ocean.
In order to assess the uncertainty in projections, we made threeother simulations with different SST spatial patterns in the future.
Three SST patterns are obtained by a cluster analysis of 28 CMIP5

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43

Fig. 1. Annual-mean sea surface temperature changes (K) between the present (1979–2003 average, historical experiment) and the end of the 21st century (2075–2099
average, RCP 8.5 experiment), for (a) the composite of total 28 models, and (b, c, d) the composites of the three clusters of the models. (e, f, g) Differences for each clusterfrom the total mean.

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A. Kitoh, H. Endo / Weather and Climate Extremes 11 (2016) 41–52

RCP8.5 experiments (Mizuta et al., 2014), and are shown in Fig. 1.
In a clustering procedure, an equal weight is given in all 28
models. Values are normalized so that the tropical (30°S to 30°N)
mean SST changes from the present (1979–2003 of historical experiment) to the end of the 21st century (2075–2099 of RCP8.5
experiment) become the same as that in the total 28 models mean.
Then clustering is performed using normalized tropical SST
changes because the effect of tropical SST should be larger thanthat of the mid- to high-latitude oceans. The clustering is ﬁnishedwhen the ﬁnal three clusters are obtained. Finally, 8, 14, and6 models are classiﬁed as Cluster 1, Cluster 2, and Cluster 3, respectively. Cluster 1 is characterized by a nearly uniform warmingin the both hemispheres, while cluster 3 is dominated by a larger
warming in the Northern Hemisphere than in the Southern
Hemisphere. Cluster 2 shows a larger warming over the central
equatorial Paciﬁc (so-called El Niño-like pattern). The CMIP5
models tend to show greater precipitation increases over larger
SST warming (“warmer-get-wetter” pattern). These characteristicsof the three clusters are discussed in Mizuta et al. (2014).
For the present climate experiment, we have performed two
simulations with different initial conditions. For the future, four
member simulations with different SST patterns were performed.
Initial conditions are arbitrary chosen from a previous long-termclimate simulation because memory of atmospheric initial conditions is lost within a month or so. A few months spin-up simulation is performed before starting the main integration period. In
this study, we use the averages of two members for the present(50 years total), and the averages of four members for the future(100 years total). Sensitivity to different SST forcing on regional
precipitation changes is also discussed. The annual mean SSTchanges between the present (1979–2003 mean) and the future(2075–2099 mean) averaged in the tropics (30°S to 30°N) is set to2.74 °C, which is the average of 28 CMIP5 models of the RCP8.5
experiments (Mizuta et al., 2014). Simulated global annual meansurface air temperature changes by the MRI-AGCM3.2 between thepresent and the future is 3.49 °C.

Table 1Deﬁnition of the precipitation extreme indices.
Index Unit
Pav
SDII

R5d
R1d
CDD

Deﬁnition

mm/day Annual mean precipitation
mm/day Simple daily intensity index: Annual total precipitation divided by the number of wet days (daily
precipitationZ 1 mm)
mm
Annual maximum 5-day precipitation totalmm
Annual maximum 1-day precipitation totalday
Annual maximum number of consecutive dry days (daily
precipitationo 1 mm)

Fig. 2. Regional domain used in this study.

change ratio for the indices, the indices on their original grid areaveraged for different regions for the present (1979–2003) and thefuture (2075–2099), and then the changes in the regionally averaged indices are divided by the corresponding regionally averaged
indices in the present.

3. Evaluation of present-day climate
2.3. Observed data
For veriﬁcation of the simulated precipitation, we used two
observed dataset. One is the 0.25-degree resolution TRMM3B42V7 (Huffman et al., 2007) for the period 1998–2013. The otheris the GPCP-1DD v.1.2 of the Global Precipitation Climatology
Project (GPCP) with a 1-degree by 1-degree grid (Huffman and
Bolvin, 2009). This data set also covers 1998–2013. The data period
is different between the observed data and the model simulation,
but does not affect our study as we investigate climatologicalstatistics.
2.4. Data processing
We used ﬁve precipitation indices, out of which four extreme
precipitation indices are the ones suggested by the Expert Team on
Climate Change Detection and Indices (ETCCDI; Klein Tank et al.,2009), as shown in Table 1: average precipitation (Pav), simple
precipitation daily intensity index (SDII), annual maximum 5-day
precipitation total (R5d), annual maximum 1-day precipitation
total (R1d), and annual maximum consecutive dry days (CDD).
All the precipitation indices for the observations and models
are calculated on their original grid. In addition, precipitation indices calculated from model data spatially averaged into 1-degreeby 1-degree boxes are also calculated to check their resolution
dependency.
Regional analysis is performed over the twenty-two land subregions used in the IPCC Fourth Assessment Report (Christensen
et al., 2007), as shown in Fig. 2. For calculation of the regional

MRI-AGCM3.2 shows a good skill in simulating dynamical andhydro-meteorological ﬁelds such as monsoon precipitation. The
model also reproduces various characteristics of tropical cyclones
such as their intensity and global distribution (Mizuta et al. 2012).
Here we evaluate ﬁve extreme precipitation indices, Pav, SDII,
R5d, R1d and CDD, whose deﬁnition is shown in Table 1. Fig. 3
shows global distributions of these ﬁve indices based on daily
precipitation data both for observations and the 20-km mesh MRIAGCM3.2. Here extreme precipitation indices based on TRMM3B42 are shown as 16-year averages for the period 1998–2013. We
note that quantitatively there is large discrepancy in values between the two observed dataset, namely TRMM-3B42 and GPCP1DD, where GPCP-1DD shows considerably smaller amount inprecipitation extremes, as will be shown later.
The model has reproduced the annual mean precipitation (Pav)very well in its geographical pattern (Fig. 3a and f). Simulated Pav
averaged for 50 °S to 50 °N overestimated the TRMM observations
by 10%. The MRI-AGCM3.2 shows maxima in annual precipitation
over the oceans, such as the western tropical Paciﬁc, Intertropical
Convergence Zone (ITCZ), South Paciﬁc Convergence Zone (SPCZ),
the Indian Ocean, and mid-latitudes to the south of Japan and to
the southeast of North America. Over land the model reproduces
heavy rainfall area over Amazon. The 20-km mesh high-resolution
enables a narrow precipitation area along the southern peripheryof Himalaya. As discussed in Mizuta et al. (2012), monsoonalseasonal migration of precipitation zones between winter andsummer is also well captured.
There is quantitatively large discrepancy in simple daily

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Fig. 3. (left) Observed and (right) simulated annual precipitation indices. (a, f) Pav, (b, g) SDII, (c, h) R5d, (d, i) R1d, (e, j) CDD. Observations are based on TRMM-3B42 for theperiod 1998–2013. See Table 1 for units. Area averages for 50°S to 50°N are shown in upper right corner of each ﬁgure.

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A. Kitoh, H. Endo / Weather and Climate Extremes 11 (2016) 41–52

precipitation intensity (SDII) between the model and simulations(Fig. 3b and g). The MRI-AGCM3.2 signiﬁcantly (more than 20% in
the 50 °S to 50 °N mean) underestimated the TRMM observations.
As the annual precipitation is about 10% larger in the model, thismeans the number of wet days (daily precipitationZ1 mm) being
overestimated in the model, i.e., too many wet days with moderate-to-light precipitation (Mizuta et al. 2012). This too manydrizzle-day syndrome (models produce precipitation too frequently and too lightly) is found in many GCMs (Dai, 2006; Stephens et al., 2010). This may be related to models’ horizontal resolution, because precipitation is calculated for a considerablylarger domain than actual precipitating clouds.
Simulated annual maximum 5-day precipitation total (R5d)and annual maximum 1-day precipitation total (R1d), on the other
hand, matches well the observations (Fig. 3 c vs. h and Fig. 3d vs.
3i). However, the model overestimated both R5d and R1d over theeastern tropical Paciﬁc. TRMM observations show the maxima
over the Philippines Sea, the South China Sea, and the Bay of
Bengal. The model tends to underestimate R5d and R1d over the
maritime continent and at the equator.
The current model (MRI-AGCM3.2) is much better than theprevious version of the model (MRI-AGCM3.1). For example, theR5d value in SAS was 104.3 mm in MRI-AGCM3.1 (Kamiguchi et al.,2006) while it is 173.6 mm in our results. As TRMM and GPCP
show 184.3 mm and 157.8 mm, respectively, our model gives muchcloser agreement with the observations.
Fig. 3e and j shows the CDD, maximum number of consecutive
dry days (daily precipitationo1 mm). The model generally reproduces CDD well.
Fig. 4 shows the regional averages of observed and simulated
annual precipitation indices. Twenty-two regional domains usedin this study are shown in Fig. 2. Here we show two observations
based on TRMM-3B42 and GPCP-1DD. Note that TRMM data does
not cover high-latitudes. In order to check the resolution difference between the two observed datasets, simulated data re-gridded into 1-degree by 1-degree (red crosses) are also plotted together with original 20-km mesh data (black crosses). Spatial
averaging into 1-degree by 1-degree causes the reduction in SDII
and R1d. However, it does not affect much R5d, and very little inPav. Differences between TRMM-3B42 and GPCP-1DD are particularly large in R1d, and larger than the differences between theoriginal and re-gridded simulated values. This implies that thedifferences between TRMM-3B42 and GPCP-1DD are not solely
responsible for their resolution.
Comparison of regionally averaged Pav reveals that the MRIAGCM3.2 reproduces regional annual mean precipitation amountvery well (Fig. 4a). The model tends to overestimate Pav over dry
regions (SAH, CAS, ALA, CGI, WNA). In other tropical and mid-latitude wet regions, the model reproduces the observed values
within 20%.
Maxima in simulated Pav are found in SEA and AMZ, followedby SAS, as in the observations. Sillmann et al. (2013a) noted that
CMIP5 models underestimated the total annual precipitation in
SEA and overestimated in AMZ compared with HadEX2 dataset(Donat et al., 2013). Although we are using different observations,these biases are not seen in MRI-AGCM3.2 simulations.
Fig. 4b shows the regional means of SDII. The SDII estimated by
GPCC-1DD are smaller than that calculated by TRMM-3B42 in all
regions where data is available. Even with this large uncertainty inthe observations, simulated SDII is underestimated in almost all
regions. Exceptions are found in cold regions such as ALA, CGI and
WNA. Largest SDII is observed in SAS, followed by SEA, while total
precipitation (Pav) is largest in SEA, not in SAS. The MRI-AGCM3.2
simulated these characteristics.
Fig. 4c and d shows the regional means of R5d and R1d. The
model reproduces regional differences in these two extreme

Fig. 4. Regional averages of observed and simulated annual precipitation indices in(a) Pav, (b) SDII, (c) R5d, (d) R1d, (e) CDD. Observations are based on TRMM-3B42(black triangles) and GPCP-1DD (red triangles) for the period 1998–2013. Simulated
data re-gridded into 1-degree by 1-degree (red crosses) are also plotted together
with original 20-km mesh data (black crosses). (For interpretation of the references
to color in this ﬁgure legend, the reader is referred to the web version of thisarticle.)precipitation indices, and simulated values are found in between
the two observations in most areas. Maxima in R5d and R1d arelocated in SEA and SAS both in the observations and the model.

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47

There are ambiguities between TRMM and GPCP, namely, the R1d
maximum is in SEA followed by SAS in TRMM but in opposite
order in GPCP. The MRI-AGCM3.2 simulates the R1d maximum in
SAS followed by SEA.
Regional distribution of CDD is also well simulated by the MRIAGCM3.2 (Fig. 4e). Small CDD values in SEA are noted both in theobservations and model simulation. A relatively long CDD appears
in SAS, where mean value as well as excessive precipitation islarge.
Overall ﬁve precipitation extremes indices simulated in MRIAGCM3.2 in its present-day climate experiment are in good
agreement with those observed. In the next section, we investigate
future changes in these indices at the end of the 21st century in
RCP8.5 scenario.

4. Future changes in precipitation extremes
Fig. 5 shows the projected future changes in ﬁve precipitation
indices at the end of the 21st century, as indicated by percent
changes from the present values. The present corresponds to theaverage of two members for 1979–2003. The future is the averageof four members for 2075–2099. Shadings denote where all four
future members have the same sign of change compared to theaverages of present-day simulations.
Global-scale spatial pattern of annual mean precipitation
changes (Fig. 5a) is very similar to that of the CMIP5 multi-model
mean assessed in IPCC (2013) and other references. Annual mean
precipitation is projected to increase in the high latitudes, theequatorial Paciﬁc Ocean and many of mid-latitude wet regions,while decrease in many mid-latitude and subtropical dry regions.
This similarity, at least in its large-scale features, can be understood by the fact that our experiment strategy uses the CMIP5
multi-model mean SST changes and precipitation changes arestrongly affected by SST anomalies (a warmer-get-wetter pattern;
Xie et al., 2010; Chadwick et al., 2013).
Projected future changes in SDII (Fig. 5b) generally show a similar geographical pattern to that in Pav, but with smaller area innegative changes. The Mediterranean and the region to thesouthwest of Australia are among such areas. Area with negative
future changes becomes much smaller in more extreme precipitation indices of R5d and R1d (Fig. 5c and d). Globally averagedchange ratio become larger in R5d (18.72%) and more in R1d(25.70%) than that in SDII (10.42%) and Pav (7.75%). Normalized by
the global annual mean surface air temperature change, theycorrespond to 2.22%/°C (Pav), 2.99%/°C (SDII), 5.36%/°C (R5d) and7.36%/°C (R1d), respectively. The projected 18.72% increase in R5d
for the period 2075–2099 in RCP8.5 scenario is within CMIP5
models range (Sillmann et al., 2013b). Almost all land grid points
show positive changes in R5d and R1d with some exceptions nearsome coastal areas.
Fig. 5e shows the future changes in CDD. Annual CDD is projected to decrease in the high latitudes, eastern part of the Eurasian continent, and some oceanic areas such as the easternequatorial Paciﬁc. Areas with increasing CDD are much broader
than where Pav decreases. This means that there are regions
where annual mean precipitation is projected to increase, and at
the same time the number of consecutive dry days becomes longer
as already noted (IPCC, 2013).
Among the four SST clusters experiments, there are some differences in the magnitude of changes in extreme precipitation
indices, although spatial patterns of precipitation changes are similar each other. It is found that the cluster 3 experiment gives thelargest R1d, R5d and SDII, while the cluster 1 experiment gives theleast. For example, the global mean R1d ranges from 23.95%(cluster 1) to 26.86% (cluster 3). The cluster 3 SST experimentFig. 5. Future change ratio (%) in (a) Pav, (b) SDII, (c) R5d, (d) R1d, (e) CDD. Global
averages are shown in upper right corner of each ﬁgure. Shadings denote where all
four future members have the same sign of the change. A spatial nine-point smoothﬁlter is applied to reduce small-scale noise. Global average values are shown inupper right corner of each ﬁgure.

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A. Kitoh, H. Endo / Weather and Climate Extremes 11 (2016) 41–52

Fig. 6. As in Fig. 5 but for (a–e) June–July–August, and (f–j) December–January–February.

A. Kitoh, H. Endo / Weather and Climate Extremes 11 (2016) 41–52

resulted in heaviest precipitation in the central and easternequatorial Paciﬁc. It should be noted that uncertainty in future SSTchange pattern results in some quantitative differences.
Fig. 6 shows the percent changes in the ﬁve precipitation indices but for June–July–August (JJA) and December–January–February (DJF) seasons. Changes in Pav are again generally similar to
CMIP5 results. Seasonal shift of projected changes is seen. Negative
Pav anomalies cover whole Europe in JJA, while they are found in
the Mediterranean region and North Africa in DJF. In South Africa,

49

negative precipitation changes are projected in JJA season. In
wintertime northern America, negative Pav anomalies are projected in southwestern US and Mexico. Over Amazon, precipitation
will increase in summer (DJF), but decrease in winter (JJA).
The above contrast between summer and winter can also beseen in SDII, R5d and R1d in the low-latitudes where extremely
large precipitation is projected to increase in the summer hemisphere land regions, and decrease in the winter hemisphere. Thechanges with opposite sign to CMIP5 projections are seen in theFig. 7. Future changes of regionally averaged precipitation indices (a–e) and their change ratio (%) (f–j). Projections using the future SST pattern of the 28 CMIP5 multi-model
mean, cluster 1, cluster 2, and cluster 3 are represented in black, red, green, and blue, respectively. (For interpretation of the references to color in this ﬁgure legend, thereader is referred to the web version of this article.)

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A. Kitoh, H. Endo / Weather and Climate Extremes 11 (2016) 41–52

western tropical Paciﬁc where SDII, R5d and R1d are projected todecrease in summer (JJA). Importance of coupled ocean–atmosphere process in the western tropical Paciﬁc is demonstrated forseasonal prediction because atmospheric feedback on SST is critical in this region (Wang et al., 2005). Therefore, disagreement
projected precipitation changes between the CMIP5 models andour experiment can be due to our experiment setting where weused ﬁxed SST as lower boundary conditions. Large percentagechanges appear in some dry regions such as Sahara due to very
little precipitation in the present climate. CDD is projected to increase in northern summer in Europe, north Asia and North
America.
Next we investigate future changes in regional average annual
precipitation indices (Fig. 7). As we have made four members simulations for the future climate projections with different SST
patterns, all four results are shown to check uncertainty of future
regional precipitation extremes projections.
Fig. 7a shows the regionally averaged annual mean precipitation changes between the end of the 21st century and present,
while Fig. 7b shows the percent change ratio. Annual mean precipitation (Pav) is projected to increase in almost all land domains,but it is projected to decrease in SAF, SEM and CAM. Regions withmost consistent changes in Pav are SAS, ALA and CGI where
standard deviation among four realizations is less than 10% of theensemble mean changes. Large scatter among four realizations isfound in AMZ, CAS, SAF, NAU and SEA. In some of the above domains, different SST patterns used have resulted in large precipitation changes, possibly related to large-scale circulation
changes in the tropical Paciﬁc. In AMZ, even sign of Pav change isdifferent among realizations where the experiment with thecluster 2 SST shows decreased Pav while other members show
increased Pav. The cluster 2 SST has a larger SST warming in thecentral and eastern tropical Paciﬁc. This strong El Niño-like SST
pattern may have resulted in precipitation decrease, which isconsistent with the fact that in interannual time scale Amazon
tends to have less precipitation in El Niño years. The SAF is also
affected much by the SST patterns used. The cluster 1 SST has alarge warming around South Africa than other clusters. Experiments with other SST clusters resulted in a Pav decrease in SAF,
while warmer SST in the cluster 1 SST resulted in near zero precipitation changes.
Large positive Pav changes in magnitude are projected in SEA
and SAS. Scatter among four experiments is large in SEA, where
the experiment with the cluster 2 SST shows least positive Pav
increase. Large positive ratio changes are shown in cold regions inhigh latitudes (ALA, CGI, NAS) and high mountain area (TIB).
Heavy precipitation indices (SDII, R5d, R1d) increase in all regional domains, even where mean precipitation decrease (SAF,
SEM, CAM). SAS is the domain of the largest extreme precipitation(R5d and R1d) increase, followed by WAF, SEA. ENA, CNA and AMZ.
SEM is the domain of the least increase in R5d.
It is noted that the largest SDII increase rate in SAH is due to alarge increase rate in Pav while an almost no change in thenumber of wet days (daily precipitation amount Z1 mm), which isdistinct in the summer season (not shown). WAF and SAH are thedomains of largest ratio increases in R5d and R1d. Scatter amongexperiments in R1d and R5d is relatively small in SEA. This is incontrast to Pav, in which SEA shows large uncertainty amongexperiments.
Large increases in CDD are projected in SAF, AMZ, SEM and
CAM. A projected increase in dry days over Amazon is consistent
with Kamiguchi et al. (2006) who used the MRI-AGCM3.1 with
SRES A1B multi-model mean SST anomalies. In our study, though,there are large scatter in AMZ among experiments associated withnorth-south contrast of future SST changes. Projected increase in
CDD over Amazon is longest in cluster 3 where the Northern

Hemisphere SST warms more than the Southern Hemisphere SST.
More northward shift of ITCZ over the tropical Atlantic Ocean incluster 3 may have resulted in a longer spell of dry period in
Amazon.
On the other hand, large decreases in CDD are projected in TIB,
SAH, CGI, EAS, ALA and NAS. In EAS, a decrease in CDD mainly
comes from wetter condition in winter (Fig. 6).
As already noted, general tendency of future precipitation extremes projected by MRI-AGCM3.2 is consistent with CMIP3 andCMIP5 multi-model ensembles. However, inter-model variability islarge in some regional domains. For example, model agreement in
R5d changes is less in SAH and CAM in CMIP5 model, in whichmore than a quarter of models projects changes in opposite signs(Sillmann et al., 2013b). Uncertainty becomes larger in CDDchanges (Sillmann et al., 2013b), being consistent with our results.
Our result is generally consistent with the former ﬁndings with
the MRI-AGCM3.1 (Kamiguchi et al., 2006) although quantitative
differences exist due to model improvement and different scenarios used.

5. Concluding remarks
In this paper, we used the four-member ensemble simulation
with the global 20-km mesh AGCM at the end of the 21st century(2075–2099) under the RCP8.5 scenario. The present-day climate
simulation showed good correspondence in magnitude of precipitation extremes indices such as R5d as compared with theobserved estimates. A high spatial resolution of the model enabled
the reproducibility of various weather systems (Oouchi et al.,2006; Murakami and Sugi, 2010), but updated physics such as
cumulus parameterization improved it quantitatively (Mizuta
et al., 2012; Murakami et al., 2012). Projected changes are shownin Murakami et al. (2012).
Spatial patterns of projected future changes in precipitation
extremes are generally similar to that of annual mean precipitation, but area with decreasing becomes much smaller in more
extreme precipitation indices of R5d and R1d. Globally averagedchange ratio, normalized by the global annual mean surface airtemperature change, is found to become larger in R5d (5.36%/°C)
and more in R1d (7.36%/°C) than that in SDII (2.99/°C) and Pav(2.22%/°C).
For the future projection we used four different SST change
patterns derived from CMIP5 model projections (Mizuta et al.,2014). It is found that some regions are strongly affected by SST
patterns. For the Pav changes, largest variability is found inSoutheast Asia for its magnitude, and in Amazon for its ratiochange. Amazon is the only regional domain where some member
experiment yielded different signs in changes. For R5d, South Asia
is the domain with largest increase in magnitude. Large scatter
among experiments is found in CDD. In this paper, we did not
perform detailed analysis of atmospheric circulation changes associated with different SST change patterns. This deﬁnitely should
be a future work.
In this experimental setting, we only covered some uncertainty
coming from future SST change patterns. The different model
physics is another source for uncertainty in future climate projections. Endo et al. (2012) had performed such an attempt andshowed that large uncertainty in future precipitation projections
in South Asia and Southeast Asia come from differences in model
physics such as cumulus convection schemes.
We have made four ensemble experiments for future projections, which is not enough to cover uncertainty range and to get
probabilistic regional climate information. About dozens or 100
realizations may be needed to achieve such climate change information. The 20-km mesh GCM is too heavy to perform many

A. Kitoh, H. Endo / Weather and Climate Extremes 11 (2016) 41–52

realizations. Instead we use the 60-km mesh version of the samemodel for ensemble experiments with many members. Although
the 60-km mesh model itself has some limitation to quantitatively
reproduce tropical cyclone intensity, some precipitation extremes
are reasonably simulated (Endo et al., 2012). Therefore, a use ofboth 20- km and 60- km models would deliver meaningful
information.
In our experimental setting, SST is prescribed as lower
boundary conditions in the present and future simulations. The
absence of air–sea interaction can cause distorted precipitationﬁeld and associated atmospheric circulations over certain regions
such as the western tropical Paciﬁc (Wang et al., 2005). This also
may have resulted in too stronger tropical cyclones and associatedprecipitation and wind extremes. An introduction of air–sea introduction may remedy this problem and is under consideration.

Acknowledgments
This work was conducted under the Development of BasicTechnology for Risk Information on Climate Change of the SOUSEIProgram of the Ministry of Education, Culture, Sports, Science, andTechnology of Japan.

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