Ito et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-020-00394-4(2020) 7:77

RESEARCH ARTICLE

Progress in Earth andPlanetary Science

Open AccessComparison of regional characteristics ofland precipitation climatology projected by
an MRI-AGCM multi-cumulus scheme andmulti-SST ensemble with CMIP5 multimodel ensemble projections
Rui Ito1,2* , Tosiyuki Nakaegawa2 and Izuru Takayabu2

Abstract
Ensembles of climate change projections created by general circulation models (GCMs) with high resolution are
increasingly needed to develop adaptation strategies for regional climate change. The Meteorological ResearchInstitute atmospheric GCM version 3.2 (MRI-AGCM3.2), which is listed in the Coupled Model Intercomparison Project
phase 5 (CMIP5), has been typically run with resolutions of 60 km and 20 km. Ensembles of MRI-AGCM3.2 consist ofmembers with multiple cumulus convection schemes and different patterns of future sea surface temperature, andare utilized together with their downscaled data; however, the limited size of the high-resolution ensemble maylead to undesirable biases and uncertainty in future climate projections that will limit its appropriateness andeffectiveness for studies on climate change and impact assessments. In this study, to develop a comprehensive
understanding of the regional precipitation simulated with MRI-AGCM3.2, we investigate how well MRI-AGCM3.2
simulates the present-day regional precipitation around the globe and compare the uncertainty in future
precipitation changes and the change projection itself between MRI-AGCM3.2 and the CMIP5 multiple atmosphere–
ocean coupled GCM (AOGCM) ensemble. MRI-AGCM3.2 reduces the bias of the regional mean precipitation
obtained with the high-performing CMIP5 models, with a reduction of approximately 20% in the bias over the
Tibetan Plateau through East Asia and Australia. When 26 global land regions are considered, MRI-AGCM3.2
simulates the spatial pattern and the regional mean realistically in more regions than the individual CMIP5 models.
As for the future projections, in 20 of the 26 regions, the sign of annual precipitation change is identical between
the 50th percentiles of the MRI-AGCM3.2 ensemble and the CMIP5 multi-model ensemble. In the other six regions
around the tropical South Pacific, the differences in modeling with and without atmosphere–ocean coupling may
affect the projections. The uncertainty in future changes in annual precipitation from MRI-AGCM3.2 partiallyoverlaps the maximum–minimum uncertainty range from the full ensemble of the CMIP5 models in all regions.
Moreover, on average over individual regions, the projections from MRI-AGCM3.2 spread over roughly 0.8 of theuncertainty range from the high-performing CMIP5 models compared to 0.4 of the range of the full ensemble.
Keywords: Regional precipitation, Projection uncertainty, MRI-AGCM, CMIP5, Future climate change* Correspondence: rui.ito@jmbsc.or.jp
1
Japan Meteorological Business Support Center, 1-1 Nagamine, Tsukuba,
Ibaraki 305-0052, Japan
2
Meteorological Research Institute, Japan Meteorological Agency, 1-1
Nagamine, Tsukuba, Ibaraki 305-0052, Japan© The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License,
which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give
appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate ifchanges were made. The images or other third party material in this article are included in the article's Creative Commonslicence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commonslicence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain
permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

1 Introduction
To establish mitigation and adaptation strategies for
future climate change, studies on climate change and assessment of its impacts have been conducted worldwide.
These recent studies have frequently used future climate
projections simulated by multiple general circulation
models (GCMs) in the framework of the Coupled Model
Intercomparison Project (CMIP); e.g., the CMIP phase 5(CMIP5; Taylor et al. 2012). While the projections from
GCMs and these study results are provided with 100-km
or coarser resolution, strategies must be developed forregional and local scales. Thus, there is a scale mismatch
between these studies and stakeholders.
Dynamical and statistical downscaling techniques may
be used to overcome the mismatch, yet confidence inthe resulting scenarios is not always increased by thedownscaling (Wilby and Dessai 2010; Hall 2014;
Shepherd 2014). The downscaled climate information ata regional scale can include regional processes and theeffects of complex terrain that GCMs cannot resolve.
However, bias in the large-scale circulation simulated by
GCMs leads to bias in the downscaled atmosphere fromthe GCM output (Hall 2014). Moreover, regional climate
models (RCMs) cannot represent phenomena with large
temporal or spatial scales beyond their boundaries nor
phenomena that are not well-represented in GCMs, suchas tropical cyclones. The spatial patterns of the regional
precipitation climatology from downscaling experiments
strongly depend on the pattern from GCMs (Šeparović
et al. 2013) as do the regional future changes (Inatsu
et al. 2015; Karmalkar 2018). Therefore, GCMs with
sufficiently high horizontal resolution are required tosimulate the climate realistically from global to regional
scales. The added value obtained by using enhancedresolution GCMs for various climate variabilities (e.g.,
in reproducing large-scale circulation features like the
El Niño–Southern Oscillation) has been reviewed byHaarsma et al. (2016).
The Meteorological Research Institute (MRI) of theJapan Meteorological Agency has been developing sucha high-resolution GCM for more than 10 years (Mizuta
et al. 2006). At present, the atmospheric GCM version
3.2 (MRI-AGCM3.2; Mizuta et al. 2012) is generally run
with horizontal resolutions of 60 km and 20 km and has
successfully simulated the global distribution of tropical
cyclones (Yoshida et al. 2017) and regional characteristics such as the seasonal march of the East Asian monsoon (Kusunoki 2018). MRI-AGCM3.2 has been widely
recognized as one of the GCMs participating in CMIP5(IPCC 2013). In addition, the climate dataset generatedby MRI-AGCM3.2 has been utilized in various international programs, for instance the Coordinated Regional
Downscaling Experiment (CORDEX; https://www.cordex.
org, Gutowski Jr. et al. 2016) in the World Climate

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Research Program, and the CMIP6 project, the High
Resolution Model Intercomparison Project (HighResMIP;
Haarsma et al. 2016). In Japan, domestic impact assessments and adaptation measures are often based on the
climate simulations by MRI-AGCM3.2.
Climate simulations by MRI-AGCM3.2 have been
designed to reproduce well regional-scale climatology
and extreme events with reasonable quality and then toinvestigate their future changes (Kitoh et al. 2016).
Present-day climate simulations have been performed
using an observed sea surface temperature (SST) dataset
while future climate simulations have used various future change patterns of SST and sea ice estimated fromthe CMIP simulations. In general, prescription of observed SST in AGCM simulations can reduce SST bias
and the consequent systematic bias in the present-day
climate simulations relative to coupled atmosphere–
ocean GCMs (AOGCMs). The feedback by the atmosphere–ocean coupling process, however, is not simulated
in an AGCM, which can lead to distorted precipitation
fields in some regions (He and Soden 2016; Kitoh et al.
2016). As well as the ensemble with different SSTchanges, an ensemble with different cumulus convection
schemes has been created with the 60-km resolution
MRI-AGCM3.2 (MRI-AGCM3.2H) because the computational resources required are relatively small compared to
the 20-km resolution MRI-AGCM3.2 (MRI-AGCM3.2S).
Thus, the ensembles with MRI-AGCM3.2 can be used toassess future projections and their uncertainty arisingfrom the differences in SST changes and model physics.
In addition to its good reproduction of tropical cyclones,
the performance of MRI-AGCM has been investigated forclimatology and extreme events on various spatial scales(e.g., Mizuta et al. 2012). Focusing on the assessment ofsimulated precipitation and its future change, Kusunoki(2017) indicated that MRI-AGCM3.2H performs well in
simulating global precipitation, comparably to or better
than atmospheric-MIP (AMIP) models in the CMIP5
archive. Kitoh and Endo (2016) and Mizuta et al. (2017)assessed global precipitation, including extreme weather
events, using MRI-AGCM3.2S and MRI-AGCM3.2H,
respectively. The high resolution of MRI-AGCM3.2 hasallowed realistic simulations of regional precipitation (e.g.,Endo et al. 2012, 2017; Kusunoki and Mizuta 2013; Ose
2017, 2019; Surendran et al. 2019). For instance, Endoet al. (2012) reported that MRI-AGCM3.2 at both resolutions realistically simulates the present-day precipitation
over several areas in Asia, and discussed the contributionsof SST changes and model physics to the uncertainty infuture changes of regional precipitation using MRIAGCM3.2H. Okada et al. (2017) and Kusunoki (2018)
evaluated the modeled seasonal march of the rain bandthat appears in the East Asian monsoon season. In
addition, Okada et al. (2017) showed that the difference inIto et al. Progress in Earth and Planetary Science(2020) 7:77

the future SST patterns in the tropics affects the precipitation change over Japan using MRI-AGCM3.2S
projections.
While the ability of MRI-AGCM to accurately represent
precipitation climatology over the globe and specific regions has been demonstrated, a comprehensive understanding of its ability and the projections of future precipitation
change in regions around the globe has yet to be established. Moreover, there has been insufficient discussion of
the global and regional properties of the uncertainty infuture projections with the MRI-AGCM3.2 ensemble. For
impact assessments of climate change, information about
the most severe situations is very useful. The MRIAGCM3.2 ensemble has a limited size of about 10 members due to its high horizontal resolution. The limitationsof a small ensemble and also a single model may lead to an
undesirably biased future projection with undesirably small
uncertainty. The CMIP provides an ensemble of opportunity, although it is not completely clear that it provides thefull uncertainty in climate projections (Knutti 2010). Thus,
a comparison with the uncertainty from the CMIP ensemble is helpful to examine the limitations of the MRIAGCM3.2 ensemble and, furthermore, gives information
about whether a single AGCM can cover the uncertainty
from the CMIP ensemble.
In this study, we use a comparison with the CMIP5
ensemble to comprehensively evaluate the regional precipitation climatology over the globe simulated by MRIAGCM3.2. The precipitation climatology is the output
most frequently analyzed in impact studies. To investigate
the extent to which the MRI-AGCM3.2 projections areadequate for discussing regional precipitation changes, aswell as projections of severe situation, our evaluation isconducted with respect to two points: how well does thesimulated present-day precipitation represent the observations over individual regions, and to what degree does the
MRI-AGCM3.2 ensemble capture the uncertainty in the
future changes of regional precipitation projected with the
CMIP5 multi-model ensemble (MME). Our principal purpose is to identify the characteristics of future changes
and their uncertainty at a regional scale. Thus, we analyze
the CMIP5 AOGCM ensemble throughout our analysis,
instead of AMIP-type model simulations that are identicalwith the MRI-AGCM3.2 simulations. Evaluations of thepresent-day precipitation are conducted to identify model
performance in representing the observations. Then,based on this performance, we create various subsets of
the CMIP5 models and compare the future projections
with them. In addition, comparisons between MRIAGCM3.2 and the CMIP5 AOGCMs can provide information on the advantage of forcing by observed SST for
simulations at regional scale. Comparisons of the presentday precipitation between MRI-AGCM3.2 and AMIP
models are given in the Supplementary Material.

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2 Methodology
2.1 Climatological dataset from MRI-AGCM3.2

The climatological dataset used in this study is simulatedusing two horizontal resolutions of MRI-AGCM3.2 as
described above. MRI-AGCM3.2H has a horizontal resolution of TL319 (triangular truncation at 319 waves; ~ 60km) and 64 vertical layers with the top at 0.01 hPa, and
MRI-AGCM3.2S has a resolution of TL959 (~ 20 km)
with the same vertical structure as MRI-AGCM3.2H.
Three different cumulus convection parameterization
schemes are implemented in MRI-AGCM3.2H: theKain–Fritsch (KF) scheme (Kain and Fritsch 1990),
the Arakawa–Schubert (AS) scheme (Randall and Pan
1993), and the Yoshimura (YS) scheme (Yoshimura
et al. 2015). MRI-AGCM3.2S was only run using the
YS scheme.
Present-day climate simulations are performed using
the UK Met Office Hadley Centre’s sea ice and SST
dataset (HadISST1.1; Rayner et al. 2003) for historical
SST and sea ice concentration observations. Futureclimate simulations are based on the representative
concentration pathway (RCP) 8.5 scenario. Using spatial
patterns of changes in SST and sea ice under the RCP8.5
scenario from the CMIP5 models, cluster analysis of thepatterns produced three clusters of models. The SSTchange patterns (C1–C3) corresponding to these threeclusters and their mean (C0) were estimated. Details of
the analysis and the method of creating the four patterns
are described in Mizuta et al. (2014). The four spatial
patterns were applied to the future climate simulations
with both resolutions of MRI-AGCM3.2. In sum, there
are three (one) ensemble members for the present-day
climate simulation from MRI-AGCM3.2H (MRIAGCM3.2S) and 12 (4) members for the future climate
simulation. The spatial pattern of standard deviation of
the four SST changes from C0 to C3 is shown in Fig. 1.
Similar patterns were obtained for the annual mean andtwo seasonal means, June–August (JJA) and December–
February (DJF). There were large deviations in highlatitude regions as well as in the northwest regions of
the Pacific and the Atlantic.
We computed annual mean precipitation PANN, boreal
summer (JJA) mean precipitation PJJA, and boreal winter(DJF) mean precipitation PDJF from monthly data over 20
years (1984–2003) for the present-day climate and 20
years of the late twenty-first century (2080–2099) for the
RCP8.5 future climate scenario. In order to scale the future change of precipitation, annual mean surface airtemperature (hereafter referred to as temperature) was
also estimated. The ensemble members used are summarized in Table 1. The MRI-AGCM3.2H ensemble members for present-day and future climate are labeled HP
and HF, respectively, and the MRI-AGCM3.2S members
SP and SF. The scheme used by each member is specified

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

Page 4 of 20(a) ANN(b) JJA(c) DJF

Fig. 1 Spatial pattern of standard deviation of four different SST change (K): three clusters and their mean. a Annual, b JJA, and c DJF

by adding the scheme name; for example, HPYS for thepresent-day simulation by HP with the YS scheme.
Regional characteristics were identified in each of 26
global land regions. The classification of the regions

follows the definition in the IPCC Special Report on
Managing the Risks of Extreme Events and Disasters to
Advance Climate Change Adaptation (IPCC SREX; IPCC
2012) and is illustrated here in Fig. 2. As in IPCC (2012),

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

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Table 1 MRI-AGCM ensemble members

we mainly analyze precipitation over land because of theimportance for impact studies. To confirm the fundamental simulation ability as a GCM, global precipitationis also evaluated. The assessment of the geographicaldistribution of precipitation is performed using the skill
scores proposed by Taylor (2001). The score S is definedasS¼

4ð1 þ RÞ;ðσ þ σ - 1 Þ2 ð1 þ R0 Þ

the observation. The maximum correlation attainable
R0 is assumed to be 1 following Kusunoki (2017). We
evaluated the simulations of the present-day precipitation averaged over 20 years on each native grid spacing of MRI-AGCM3.2H/S and also the simulations
with MRI-AGCM3.2S interpolated onto the spacing of
MRI-AGCM3.2H. Future change was estimated on
the native grid spacing of each model without the
spatial interpolation.

2.2 CMIP5 multi-model ensemble

where R is the spatial correlation coefficient between
reference observation and simulation and σ is the standard deviation of the simulation spatially normalized by

The comparisons with the CMIP5 models used the outcomes simulated by 50 AOGCMs for the present-day
climate (CMIP5Present_All) and by 42 AOGCMs for the

Fig. 2 Definition of the 26 global land regions specified in IPCC SREX (IPCC 2012)

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

RCP8.5 future climate (CMIP5RCP8.5_All) (Taylor et al.
2012). One ensemble member run for each model wasused. Annual and seasonal mean precipitation over thesame period as the MRI-AGCM3.2 dataset were computed from monthly mean data with the original horizontal resolution of each model. We then interpolated
the 20-year averaged values onto the MRI-AGCM3.2H
grid. The annual mean temperature used as the scalingof future precipitation change was also interpolated onto
the MRI-AGCM3.2H grid for the 20-year averaged
values. The models are listed in the Supplementary(Table S1).
In addition to the full ensemble of all model members,
the following subsets of high performance models were
produced: the subsets with high Taylor’s skill scores for
global precipitation (CMIPGhighS) and for regional land
precipitation (CMIPRhighS), and the subsets with smallmean absolute error (MAE) for global precipitation(CMIPGsmallE). Each of the subsets has 12 members andis the same size as the set of HF members and the members of these subsets are included in both CMIP5Present_All and CMIP5RCP8.5_All. Hence, we investigated thecharacteristics of the future projections with HF, relative
to the projections with not only the CMIP5 full set but
also the subsets with high performance.
2.3 Observational products for validation

Simulations of present-day precipitation were validated
against multiple precipitation products because of thediscrepancies in precipitation estimates among observational products reported by Sun et al. (2018). We used
the following observational datasets. The products basedon global gauge observations, which are provided overland with a grid spacing of 0.5°, are the Global Precipitation Climatology Centre (GPCC) full data reanalysis
v.7.0 (Schneider et al. 2016), the global unified gaugebased analysis by the NOAA Climate Prediction Center(CPC) v.1.0 (Xie et al. 2010), the Climatic Research Unit
Timeseries (CRU TS) v.4.03 (Harris et al. 2014), andNOAA’s precipitation reconstruction over Land
(PRECL) v.1.0 (Chen et al. 2002). Products that are estimated mainly from satellite data are the CPC Merged
Analysis of Precipitation (CMAP; Xie and Arkin 1997),
the Global Precipitation Climatology Project 1 degree
daily (GPCP-1DD) v.1.2 (Huffman et al. 2001) and theMulti-Source Weighted-Ensemble Precipitation (MSWEP)
v2.1 (Beck et al. 2019). The CMAP, GPCP-1DD, andMSWEP products are available over the full global surface
with horizontal resolutions of 2.5°, 1.0°, and 0.1°, respectively. All products cover the global land area for the period
of interest (1984–2003, 20 years), except GPCP-1DD(1997–2014, 18 years).
The MSWEP product is provided at high resolution andcovers the whole of our target period and thus wasPage 6 of 20

adopted as the reference dataset for the validation.
MSWEP has precipitation estimates generally lying toward
the mean of those from other datasets (Fig. 6 in Sun et al.
2018). The observations were interpolated onto the MRIAGCM3.2H grid. When evaluating Taylor’s score and themodel bias in the present-day precipitation by SP without
spatial interpolation, the MSWEP precipitation was interpolated onto the MRI-AGCM3.2S grid.

3 Results
3.1 Evaluation of the performance of the present-day
simulations
3.1.1 Global distribution of the simulations of present-day
precipitation

Figure 3 shows the global distribution of present-day
PANN derived from observations and the bias in the model
simulation. Compared with the mean of CMIP5Present_All
with a maximum bias of more than 1% (Fig. 3f), MRIAGCM3.2 simulates PANN with a bias of less than 0.5%
over most areas from the Tibetan Plateau through East
Asia and Australia (Fig. 3b–e). The three members ofHPs, HPALL, have a similar pattern of bias in the global
land precipitation (Fig. 3c–e). Differences in the horizontal
resolution between HPYS and SPYS are not largely responsible for the global distribution of the bias (Fig. 3b, e).
Each member of MRI-AGCM3.2 simulates more precipitation in the Pacific intertropical convergence zone thanthe observations, as pointed out by Kusunoki (2017), andalso over the Indian Ocean with different patterns for thethree members. An overestimate of PANN is notable overthe central North Pacific and the western tropical Pacific,
which is not seen or is small in the mean of CMIP5Present_All. Additionally, there is a large difference in northern
South America where MRI-AGCM3.2 overestimates theprecipitation and the mean of CMIP5Present_All underestimates it. The spatial pattern of the bias is similar between
the means of CMIP5Present_All, CMIP5GhighS, andCMIP5GsmallE (Fig. 3f−h).
3.1.2 Regional performance of the simulations of presentday precipitation

In order to evaluate how well each member reproduces
land PANN at regional scale, Taylor’s skill scores andmodel biases in the regional mean with respect to
MSWEP in each of the 26 regions shown in Fig. 2 aresummarized in Figs. 4 and 5, respectively. We also consider the performance of CMIP5GhighS (i.e., the subset
exhibiting the highest 12 skill scores for global precipitation; Fig. 4) and CMIP5RhighS (i.e., the subset for regional
land precipitation; Fig. 5), as well as the performances of
HPAll and CMIP5Present_All. We used the skill score as
the main metric to select the subsets for comparison because
the difference of the pattern from the observations results ina large bias. Furthermore, corrections of the spatial pattern

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

Page 7 of 20(a) MSWEP(b) SPYS(c) HPAS(d) HPKF(e) HPYS(f) CMIP5All(g) CMIP5GhighS(h) CMIP5GsmallE

Fig. 3 a Observed (MSWEP) estimate of annual mean precipitation (mm day–1) and b–h model bias in the simulated precipitation for thepresent-day (1984–2003) (%). b SPYS. c–e HPs with the three cumulus convection schemes AS, KF, and YS. f–h The ensemble means of all CMIP5AOGCM members (CMIP5Present_All), the CMIP5 members with the 12 highest Taylor’s skill score for global precipitation (CMIP5GhighS), and thosewith the 12 smallest MAE in global precipitation (CMIP5GsmallE). Estimates are based on the native grid spacing of MSWEP or the model in (a–e)
and are interpolated onto the grid spacing of MRI-AGCM3.2H in (f–h)

relevant to large-scale atmospheric circulations are more difficult than corrections of the bias at a grid point (e.g., Maraun 2016). The abbreviations and region numbers ofindividual regions used are those in Fig. 2.
The scores for all HP members exceed 0.8 in 16 regions, and in an additional five regions some HPs have ascore > 0.8, but not in wet regions of AMZ (region number 7) and SEA (24), dry regions of WSA (9) and SAH(14), and a high-latitude region of CGI (2; Fig. 4). The
scores in CGI (2) are less than 0.8 but are within the discrepancy between the observations, as in the case with
Europe (11−13) and Asia (18−21). There are high scores> 0.9 in North America (3−4) and Europe (11, 13), in
addition to TIB (21), EAS (22), and SAU (26) where thebias is small in Fig. 3. The global precipitation is also
represented with a score above 0.9. Such a high score >0.9 is approximately equal to or greater than the highest

score of CMIP5Present_All. The CMIP5GhighS ensemble includes members with scores lower than the 50th
percentile of CMIP5Present_All in all regions and also for
global land (GLB Land in Fig. 4). That is to say, CMIP5GhighS can represent the pattern of global PANN realistically but does not do so well for the patterns of regional
land PANN. HPAll, by contrast, represents both patterns
closer to the observations because the average score of
0.94 over HPAll for global PANN exceeds any scores from
CMIP5Present_All (GLB in Fig. 4) and all scores of HPAll
for regional land PANN are above the lowest score of
CMIP5GhighS, apart from the three regions WSA (9),SAS (23), and SEA (24). All HP members show a higherscore than the top three scores of the 75th percentile ormore in CMIP5GhighS, in 11 regions. Interpolating PANN
onto the MRI-AGCM3.2H grid, SPYS shows a higherscore than HPYS in 12 regions. In four regions including

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

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Fig. 4 Taylor’s skill score for annual land precipitation averaged over the period 1984–2003. Green indicates observations. Gray is CMIP5Present_Alland black CMIP5GhighS. Red indicates each member of HPAll and the dark red bar indicates the ensemble mean. Dark and light blue indicate SPYS
with and without interpolation, respectively. Circles indicate use of the YS scheme. The reference dataset is MSWEP. The region numbers andabbreviations correspond to Fig. 2. GLB and GLB Land denote global and global land precipitation, respectively. For GLB, three observations(CMAP, GPCP-1DD, and MSWEP) are plotted in green because of the availability of global precipitation

TIB (21), the score of SPYS exceeds all scores of HPAll.
Without the interpolation, the score decreases in all
regions and the decrease is 0.05 on average over theregions.
For seasonal precipitation, HPAll has a score > 0.8 in
12 regions in JJA and 19 regions in DJF (Fig. S1). The
scores in SAU (26) are greater than the highest score of
CMIP5Present_All in both the annual and the seasonalmeans.
Figure 5 shows that some HP members represent
PANN with a bias comparable to the discrepancies
between the observations in 12 regions; e.g., SouthAmerica (7–8, 10), South and Southeast Asia (22–24),
and Australia (25–26). In the three regions CGI (2), SSA(10), and CEU (12) in particular, all biases from HPAll
are within the discrepancy of the observations. In CGI
and CEU, all skill scores are also within the discrepancy(Fig. 4), indicating that HPAll represents precipitation

quantitatively and spatially close to reality. As seen in
Fig. 3, the bias in TIB (21) is more than 30% smaller inthe mean of HPAll than in the 50th percentiles of
CMIP5Present_All and also CMIP5RhighS. Similarly, the bias
is approximately 10% smaller in EAS (22) and SAS (23)and > 25% smaller in NAU (25). All biases from HPAll
are smaller than the largest bias from CMIP5RhighS in all
regions. Focusing on a quartile of CMIP5RhighS indicating the three smallest biases, the bias is larger in HPAll.
However, we should note that the combination of threemembers selected from CMIP5RhighS varies region-byregion, whereas the same HP members are evaluatedconsistently in every region.
Although HPALL simulates well the spatial patternof global and global land precipitation relative to
CMIP5Present_All (Fig. 4), the members overestimate
the precipitation, in particular, the global land precipitation with a bias comparable to the 75th

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Fig. 5 As in Fig. 4 but for model bias in regional land averaged precipitation (%). Black indicates the subset of CMIP5 AOGCMs with the 12
highest skill scores for regional land precipitation (CMIP5RhighS) except for the plots for global (GLB) and global land precipitation (GLB Land) inwhich the plots are for the subset with the 12 highest skill scores for global precipitation

percentile of CMIP5Present_All and CMIP5GhighS. Thisis because areas of negative bias are more widely distributed in the mean of CMIP5Present_All than in
HPALL (Fig. 3) and, consequently, the bias is offset
globally in CMIP5Present_All. MAE in both the global
land and sea and global land precipitation from
HPALL is smaller or comparable to the smallest MAE
from CMIP5Present_All (not shown).
Depending on the convection scheme used, PJJA simulated by HPAll are within the spread of observations in
17 regions and PDJF in 15 regions (Fig. S2). In CEU (12),all biases in the annual and the seasonal means are comparable to the discrepancies between observations.
SPYS simulates PANN with less bias than HPYS in
more than half the regions on the common MRIAGCM3.2H grids, and also PJJA in 10 regions and PDJF
in 16 regions. In addition, the bias in PANN from SPYS is
small regardless of the convection scheme used in HPAllin seven regions including TIB (21). The skill score reduces in all regions when evaluating the SPYS precipitation on the native grid (Fig. 4). On the other hand, thebias in the regional mean precipitation is almost equal incases with and without the interpolation except for
WSA (9) with the complex terrains of the Andes, where thebias reduces by 10% in the evaluation on the native grid.
This implies that there may be a difference in the spatial
pattern of precipitation between MSWEP and SPYS.
3.1.3 Model rank for the performance over the regions

To gain an overview of the performance of each model,
we ranked 55 models using MAE and skill score: threeof HPAll, the interpolated SPYS, 50 of the individualmodel members of CMIP5Present_All, and the MME meanof CMIP5Present_All. The rank of each model and the
MME mean were computed as follows. First, in each region, each model was ranked from 1 to 55 according to
its MAE or score, with rank 1 assigned to the modelwith the smallest MAE or the highest score. Secondly,for each model, the order/rank in each region was averaged over all the regions. A small overall rank corresponds to the model having a smaller MAE or a higherscore in many regions.

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

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Fig. 6 Model performance ranked using a MAE and b Taylor’s skill score. The x-axis denotes the model rank within the set of 55 models
considered and the y-axis denotes the model rank averaged over all 26 regions. Solid circles indicate HPAS (blue), HPKF (green), HPYS (red), open
circles SPYS (red) and crosses the ensemble mean of the CMIP5 multi-AOGCMs (yellow). Numbers indicate individual CMIP5 models (Table S1)

The x-axis in Fig. 6 denotes the rank from 1 to 55
based on the average rank and the y-axis denotes therank averaged over the regions. HPAll and SPYS always
rank in the top 15 models for both metrics except for
MAE in PANN from HPKF. In particular, HPYS and
SPYS are always in the top five models for the seasonal
precipitation. For the skill score, individual members of
HPs and SPYS are often ranked higher than the MME
mean of CMIP5Present_All. HPKF has the best averagerank of 13.7 on the score for PANN, and HPYS and SPYS
follow HPKF with average ranks of 14.0 and 15.2, respectively. HPYS has the best rank on the score for PJJA,
followed by SPYS and HPKF. Thus, HPAll and SPYS cansimulate the spatial pattern and the regional mean of thepresent-day precipitation close to the observations inmore regions than can the CMIP5 models. The CMIP5
models change their rank depending on the metric, andtheir ranks are not always above those of HPAll. Namely,
prescribing observed SST for a GCM contributes to amore realistic spatial pattern and amount of regional
precipitation climatology around the globe. The order ofrank is almost the same when using the AMIP-type simulations in the CMIP5 (Fig. S3), and HPAll is ranked

around the MME mean of the AMIP simulations, especially for the skill score.
3.2 Future change projection for the RCP8.5 scenario
3.2.1 Global comparison of the uncertainty in future
precipitation change

Figure 7a–d illustrates the future change of PANN
averaged over the projections by the HFAll ensemble andthe MMEs of CMIP5RCP8.5_All, CMIP5GhighS, andCMIP5GsmallE. As described in IPCC (2013), the mean of
CMIP5RCP8.5_All projects an increase of global land PANN
at the 90% confidence level in most regions of the middle- and high-latitude Northern Hemisphere and a decrease around the Mediterranean (Fig. 7b). The regionswith a significant change extend farther in HFAll than in
CMIP5RCP8.5_All (Fig. 7a), and the sign of the change coincides with the sign given by CMIP5RCP8.5_All in mostregions (Fig. 7e). In Australia and northern South America, the mean of HFAll projects an increase at the 90%
confidence level while that of CMIP5RCP8.5_All projects
an insignificant decrease (Fig. 7e). Figure 7h shows thatthe mean of HFAll projects a noticeably wetter climate
over the land than the mean of CMIP5RCP8.5_All,

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

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Fig. 7 a–d Spatial pattern of annual mean precipitation change in 2080–2099 with respect to 1984–2003 for the RCP8.5 scenario, scaled by the
globally annual averaged temperature change in each model (% K–1). Full ensembles of a the HF members (HFAll) and b the CMIP5 AOGCMs(CMIP5RCP8.5_All), and the subsets of CMIP5RCP8.5_All c with the 12 highest skill scores for global precipitation (CMIP5GhighS) and d with the 12
smallest MAE in global precipitation (CMIP5GsmallE). Crosshatched (hatched) areas in (a–d) indicate areas where changes are insignificant at the
33% (10%) level. e–g Precipitation change with HFAll but for the grids with the opposite sign of the change between the ensemble means of
HFAll and of e CMIP5RCP8.5_All, f CMIP5GhighS, and g CMIP5GsmallE (% K–1). h–j Difference of the ensemble mean of precipitation changes from HFAll
with respect to that from (h) CMIP5RCP8.5_All, i CMIP5GhighS, and j CMIP5GsmallE (% K–1)

especially from the Tibetan Plateau to East Asia, overnorthern South America, and the dry regions ofAustralia and the Sahara. The projections from the full
ensemble of the CMIP5 models and its subsets are similar over the land even when the models are selected bytheir performance on present-day precipitation (Fig. 7c,d, i, j). Although the precipitation change over the ocean
is beyond the scope of this study, we note that thechange over the tropical Pacific is different in the meansof the CMIP5RCP8.5_All and the subsets. HFAll projects a
precipitation change with an El Niño-like pattern on theensemble mean, relative to the CMIP5RCP8.5_All projections (Fig. 7h). The pattern, however, is not always
detected in the comparison with the high-performing
subset projections (Fig. 7i, j). The changes in PJJA and
PDJF show a similar pattern over the tropical Pacific(Figs. S4 and S5).
In order to identify features of the uncertainty in future precipitation changes from HFAll, we explored how
the uncertainty spread of HFAll compares with that fromthe CMIP5RCP8.5 models. Previous studies assessed theuncertainty from subsets of ensemble members using

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

the degree to which the maximum–minimum range of
uncertainty from all ensemble members is covered by
the range from the subset (McSweeney et al. 2015; Karmalkar 2018; Ito et al. 2020a). Following this methodology and expressing precipitation changes for ensemble
E as ΔP(E), we computed the fraction of the maximum–
minimum range of ΔP(HFAll) to the range of ΔP(E′),
and refer to this as a fraction of the relative range (FR).
Here, we used three ensembles as the ensemble E′: all
42 members of CMIP5RCP8.5_All, and the 12 members ofeach of CMIP5GhighS and CMIP5GsmallE.
The value of FR for HFAll relative to CMIP5RCP8.5_All,
FRAll, exceeds 0.7 around North America, southern Africa, East Asia, and Australia (Fig. 8a). Grid cells where
FRAll lies between 0.7 and 1.3 (i.e., the spread of
ΔPANN(HFAll) is within ± 30% of the spread of ΔPANN(CMIP5RCP8.5_All)), make up 11% of the global land gridcells, and grid cells with values between 0.5 and 1.5
make up 35%. As for CMIP5GhighS and CMIP5GsmallE,the percentage of the grid cells with FR within 0.7–1.3(0.5–1.5) increases up to approximately 45% (75%) (Fig.
8b, c). Therefore, selecting the high-performance CMIP5
models to represent the present-day precipitation morethan doubles the area where the uncertainty ranges of
HFAll and the selected CMIP5 models are close. The
fraction from the uncertainty of CMIP5GhighS, FRGhighS,
exceeds 0.7 over most land areas. A similar spatial pattern is seen in the fraction from CMIP5GsmallE, FRGsmallE.
Of the 12 members in each of CMIP5GhighS andCMIP5GsmallE, three members of CMIP5GhighS are not in
CMIP5GsmallE. Thus, the three members make their own
patterns, e.g., Australia and South America around thetropical Pacific where a different pattern of ΔPANN appears (Fig. 7i, j).
The tendency for a closer spread between HFAll andthe high-performing subsets of the CMIP5 models is detected in the seasonal precipitation change (Fig. S6). As
with the annual precipitation change, for the seasonal
precipitation change, the percentage of the grid cellswith FRGhighS and FRGsmallE within 0.5–1.5 increases toapproximately 70% of all land grid cells from 40% in
FRAll.

3.2.2 Uncertainty in the projected change of regional mean
precipitation

The projected future change of regional mean PANN overland is shown in Fig. 9. Here, we analyze the projections
by the MMEs of CMIP5GhighS and CMIP5RhighS, together
with the HFAll ensemble and the CMIP5RCP8.5_All MME.
Additionally, considering the effects of model resolution,
the future changes projected with SF are also considered,
which were computed from the regional mean on thenative grid of MRI-AGCM3.2S.

Page 12 of 20

There is a wide spread in the projections ΔP(CMIP5RCP8.5_All) in low-latitude regions and a narrower spread in
the middle- and high-latitude regions, as shown in IPCC
(2013) with respect to both land and sea areas. The uncertainty from ΔP(HFAll) of the annual and seasonal precipitation presents a similar picture (Fig. S7). This may be dueto the difference in the main precipitation systems; i.e., themajor systems are sub-grid scale (produced by cumulus
convection schemes) at low latitudes and grid scale atmiddle and high latitudes.
The maximum–minimum uncertainty range from
HFAll partly overlaps all the range from CMIP5RCP8.5_All,
CMIP5GhighS, and CMIP5RhighS except for CMIP5RhighSin CGI (2). The range from HFAll is 0.4 of the range
from CMIP5RCP8.5_All on average over all 26 regions; thefraction increases to 0.8 (0.73) of the range from CMIP5GhighS (CMIP5RhighS). In terms of the future change in
the global average, specifically global land precipitation,
the spread of the projections with HFAll is equal to 0.74
of the spread from CMIP5RCP8.5_All and to 0.95 of thatfrom CMIP5GhighS. Thus, the future change projectedwith HFAll roughly has an uncertainty very close to theuncertainty in the projections with the high-performing
CMIP5 models, as described above. In the individual
areas, the projections with HFAll have a wider spreadthan the range from CMIP5GhighS and CMIP5RhighS in
SAF (17) and AMZ (7), and a comparable spread in NEB(8), WAS (19), EAS (22), SEA (24), and SAU (26). On
the other hand, there is a relatively small spread of the
HFAll projections in NAS (18) and TIB (21). The projections with the SF ensemble lie within the uncertainty
from HFAll in most regions. Given that the SF ensemble
is the ensemble of only four patterns of SST change, thespread is small but the individual regional pattern of
spread resembles the spread from HFAll.
Over the Tibetan Plateau and East Asia, where a significant increase is projected by the ensemble means ofboth HFAll and CMIP5RCP8.5_All (Fig. 7a, b), the rate ofincrease is 0.5–2% larger in HFAll on average over TIB(21) and EAS (22). The rate in TIB is an additional 1%
larger in the mean of the SF ensemble than that of HFAll,which is a marked difference compared with other regions. The projected future change has the opposite signof future change between the 50th percentiles ofΔP(HFAll) and ΔP(CMIP5RCP8.5_All) in six regions aroundthe tropical South Pacific: Central and South America (6−9) and Australia (25−26). In particular, in CAM (6) andNEB (8), more than three HFAll members project an increase and different three members project a decrease sothat the future change is insignificant. On the other
hand, although there is a large uncertainty in the projections with not only CMIP5RCP8.5_All but also CMIP5GhighS and CMIP5RhighS, more than 75% of HFAll project
an increase in AMZ (7), NAU (25), and SAU (26) and a

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

Page 13 of 20(a) FRAll(b) FRGhighS(c) FRGsmallE

Fig. 8 Fraction of relative range of the uncertainty from HFAll to the range from a CMIP5RCP8.5_All, b CMIP5GhighS, and c CMIP5GsmallE. The value atthe top right of each panel denotes the percentage of grid cells with FR between 0.7 and 1.3 in the global land, and the value in theparentheses denotes the percentage between 0.5 and 1.5

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

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Fig. 9 Uncertainty estimates for future change projections of regional land mean precipitation in 2080–2099 with respect to 1984–2003 for the
RCP8.5 scenario (% K–1). Change rate is scaled by the regional land annual temperature increment. Gray plots indicate CMIP5RCP8.5_All. Yellow andgreen plots indicate CMIP5GhighS and CMIP5RhighS, respectively. Red plots indicate HFAll. Blue plots and dark blue bar indicate SPYS and theensemble mean, respectively

decrease in WSA (9). In particular, all members of HFAll
project the opposite sign to the 50th percentiles ofΔP(CMIP5RCP8.5_All) in WSA and NAU. In NAU, the50th percentile of ΔP(HFAll) is 5.3% while that ofΔP(CMIP5RCP8.5_All) is − 0.7%. Of the six regions with
opposite signs in different ensembles, the mean of HPAll
has a score > 0.8 in NEB (8) and Australia (Fig. 4) and abias comparable to the discrepancies between observations in AMZ (7), NEB (8), and Australia (Fig. 5). Thissuggests that forcing with the observed SST reduces the
SST bias in HPAll compared with the CMIP5 coupledsimulations, and consequently HPAll performs well in
representing the present-day precipitation. On the other
hand, with regard to future simulations, the differences
between the atmosphere-only model and the coupledmodel could cause the opposite sign in the future
precipitation changes in the regions. A relatively large
positive precipitation change in HFAll is consistent with
Kitoh and Arakawa (1999), who have demonstrated thatthe lack of SST and cloud–radiation feedback in

AGCMs brings about an overestimate of precipitation in
warm-SST regions.
For the global mean, HFAll projects a wetter climate
than CMIP5RCP8.5_All, especially over the land; the projections with more than half the members of HFAll
exceed the maximum changes of CMIP5RCP8.5_All andCMIP5GhighS. DeAngelis et al. (2015) have indicated that
CMIP5 models that underestimate the sensitivity ofshortwave absorption to an increase in atmosphericwater vapor tend to overestimate the precipitation increase.
From their results, the CMIP5 model developed at MRI
shows a lower sensitivity of shortwave absorption than theother CMIP5 models. The radiation scheme in that modelis almost the same as the scheme used in MRI-AGCM3.2
for computing the sensitivity. Thus, the sensitivity could tosome extent contribute to the greater increase of the precipitation by HFAll than by CMIP5RCP8.5_All.
Similar to PANN, the projections of HFAll spread over 0.35
and 0.46 of the maximum–minimum uncertainty range
from ΔPJJA(CMIP5RCP8.5_All) and ΔPDJF(CMIP5RCP8.5_All) on

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

average over all 26 regions, respectively (Fig. S7). The fraction increases to 0.78 and 0.96 of the range from ΔPJJA(CMIP5GhighS) and ΔPDJF(CMIP5GhighS), respectively.

4 Discussion
4.1 Contribution of cumulus schemes and SST forcing to
the uncertainty

The uncertainty from HFAll is estimated from the future
precipitation changes by 12 ensemble members corresponding to combinations of three different cumulus
convection schemes and four SST change patterns. In
order to discuss the sources of uncertainty in the HFAll
projections, we created two subsets, one with different
convection schemes but the same C0 SST-change
pattern (HFConv; three members) and the other with thesame YS scheme but different SST change patterns(HFSST; four members). The FR values were then estimated for the maximum–minimum uncertainty range
from the projections with HFConv and HFSST relative tothe range with HFAll (FRConv and FRSST, respectively).
FRConv (FRSST) denotes the ratio of HFConv (HFSST) covering the uncertainty from HFAll.
Figure 10 shows global distributions of FRConv andFRSST. The area where either HFConv or HFSST captures
over 0.6 of the uncertainty from HFAll is only approximately 40% of the global land, implying that the ensemble consisting of both multiple SST patterns andmultiple convection schemes is needed to cover most of
the uncertainty from the high-performing CMIP5
models. FRConv is larger than FRSST in Africa and northern South America (Fig. 10a). On the other hand, a relatively large FRSST appears around Australia and Siberia(Fig. 10b). Around Asia, Endo et al. (2012) have assessed
the contribution of each factor to the uncertainty byanalysis of variance on the same MRI-AGCM3.2H dataset. Their results indicate that the uncertainty in ΔPANN,
as well as in ΔPJJA and ΔPDJF, is mainly caused byPage 15 of 20

differences in the cumulus convection schemes in Southand Southeast Asia, which is consistent with the relatively large FRConv seen in Fig. 10a.
To investigate the atmospheric fields leading to the
spreads of the projections of ΔPANN, Fig. 11 shows thestandard deviation in future change for three atmospheric circulation variables: sea level pressure (ΔSLP),
temperature at 850 hPa (ΔT850), and geopotential
height at 500 hPa (ΔZ500). Because of the common SSTchange pattern, the deviation in ΔT850 from HFConv isglobally small except for the western part of the tropical
Pacific (Fig. 11b). On the other hand, HFConv shows arelatively large deviation in ΔZ500 over the tropical andsubtropical regions of 30°S−30° N (Fig. 11c). That is tosay, the response of ΔZ500 over the regions depends onthe cumulus convection scheme, which may relate to thedifference in the precipitation changes between the different cumulus schemes. Therefore, FRConv is large
around Africa and northern South America over 30° S−30° N. The dependence of ΔZ500 on the schemes over
30° S−30° N is seen regardless of the patterns of SSTchange; the KF (AS) scheme gives a more positive (negative) anomaly in ΔZ500 than the YS scheme (Fig. S8a).
HFSST shows a roughly similar distribution of the deviation in ΔT850 to that in the SST change (Figs. 1a and11e). Around Australia where FRSST is large, ΔSLP from
HFSST shows a large deviation (Fig. 11d). The differenceof ΔSLP between members with different SST change
patterns responds to the prescribed patterns of El Niño/
La Niña-like SST change over the Pacific (Fig. S8b). The
atmospheric response to the El Niño/Southern Oscillation (ENSO) expands over Australia and brings about arelatively large uncertainty in the precipitation changes
with HFSST.
With reference to Mizuta et al. (2014), we divided
CMIP5 models into three clusters to obtain futurechange patterns of SST and sea ice (C1–C3) for future

Fig. 10 As in Fig. 8 but for the fraction relative to the uncertainty from HFAll for a the three members of HFAll with different convection schemes(HFConv) and b the four members with different patterns of SST change (HFSST). The value at the top right of each panel denotes the percentage
of grid cells with FR over 0.6 in the global land

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

Page 16 of 20

Fig. 11 Standard deviation of the future change of a, d SLP, b, e T850, and c, f Z500. a–c Future changes with HFConv and d–f those with HFSST.
The deviation is normalized by annual variation of individual variables in the present-day climate. Hatching indicates missing dataclimate simulations with MRI-AGCM3.2. The ΔSLP
were then averaged over the models in each cluster andover all models to derive four spatial patterns of ΔSLP
corresponding to the pattern from each member of
HFSST. The spatial pattern of the deviation in ΔSLP
from the clusters of the CMIP5 models (Fig. S9) resembles the pattern from the HFSST projections overthe latitudinal band of 0°–30°S (Fig. 11d). That is,there is a similarity in the atmospheric response to
the SST change between the ensembles of MRIAGCM3.2 and the CMIP5 models, implying that thedifference in the precipitation change around
Australia between the ensembles arises from a quantitative difference in the response to a similar changeof atmospheric circulation. The uncertainty could also
be associated with differences in the convection
schemes, especially in the northern regions ofAustralia at latitudes north of 30° S (Fig. 11c). Thesemight lead to large uncertainty in NAU (25 in Fig. 9).
A large FRSST around Siberia is partially affected byteleconnections such as the Pacific–North America patterns in response to the same ENSO-like change (Horeland Wallace 1981), as is also the case around North
America and Asia (Fig. 11d). Urabe and Maeda (2014)
and Imada et al. (2017) pointed out the seasonal variations in the appearance of the teleconnection patternaround Asia. Future study is required focusing on the effects of the seasonal change of the teleconnection
pattern.

4.2 Enhanced model resolution

In order to examine the impact of the horizontal resolution of models on the simulations of PANN, we summarized the relationship between the following four
indices and the complexity in topography in the 26 regions: the extent of the change in skill score and model
bias in the regional mean from HPYS to SPYS (Fig. 12a,b), the difference in the ensemble mean of ΔPANN (Fig.
12c), and the ratio of the maximum–minimum uncertainty range from ΔPANN projected by SFYS to the range
from HFSST (Fig. 12d). The indices were estimated on
the native grid of each model. The future change is the
projected change in the regional mean. The ensemble of
SFYS and HFSST consists of the four members with thesame YS scheme and different four patterns of SSTchange. The complexity in topography is defined as thestandard deviation of the altitude represented by MRIAGCM3.2S.
The impact of the enhanced horizontal resolution isa significant decrease in model bias. The relationshipbetween the complexity and the decrease in the bias
is statistically significant at the 99% confidence level.
The difference in the ensemble means of futurechanges is positively correlated with the complexity,
although the relationship is insignificant. This is also
the case for the uncertainty range. Consequently, simulations by the high-resolution model may project agreater future precipitation increase around complex
terrain regions, with a reduction of the model bias inIto et al. Progress in Earth and Planetary Science(2020) 7:77

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Fig. 12 Relationship to the complexity of regional topography for differences of the SP(SF) simulations from those by the HP(HF) simulations. a
Skill score, b model bias, c future change of the regional mean, and d its uncertainty. R is the correlation coefficient. Asterisk denotes the 99%
confidence level

the present-day precipitation. Furthermore, there is apossibility that the uncertainty in the future projections is greater in regions with more complex
topography.
As seen in Fig. 4, the skill score is smaller acrossall regions in SPYS than in HPYS except for NEU(11) and NAU (25), regardless of the complexity. It
seems that the high-resolution model cannot simulate
the precipitation pattern better than the lowresolution one. However, Fig. 4 shows a large discrepancy in the score for the observations, with a value of
0.4 in TIB (21) and WSA (9), regions with high complexity in the topography. Thus, we cannot conclude thatthe high-resolution model performs poorly in representing
the spatial pattern. Further validation is needed using observations with a resolution of a few tens of kilometersthat is high enough to evaluate the simulations with MRIAGCM3.2S.
Note that the difference in the simulations between
various model resolutions derives from not only the
represented topography but also the precipitation systems themselves that the model is able to resolve. According to Ito et al. (2020b), focusing on the areas
around Japan that are included in EAS (22), the projection of the regional precipitation changes when considering the effect of typhoons that low-resolution models
cannot represent.
The impact of the model resolution is more effectivein the simulations of extreme indices; e.g., a seasonal
maximum of 1-day precipitation total. Similar to theneed for projections of average climate change, this isimportant for the impact assessment of climate change.

5 SummaryWe have shown that the simulations by MRIAGCM3.2 reproduce well the present-day precipitation climatology in regions around the globe and thatthe signs of future changes at the 50th percentile are
identical to the CMIP5 multi-AOGCM ensemble inalmost all regions. The projections of future precipitation change spread over roughly 0.4 of the uncertainty range from the full ensemble of the CMIP5AOGCMs and, furthermore, 0.8 of the range from thehigh-performing CMIP5 AOGCMs.
The precipitation climatology simulated by MRIAGCM3.2 is closer to observations at both global andregional scales in terms of the spatial pattern andamount. Compared with the means of the CMIP5 ensemble and the high-performing CMIP5 ensembles,
the model bias is reduced by approximately 20% forregional averages from the Tibetan Plateau throughEast Asia and Australia. Overall, MRI-AGCM3.2 has askill score of more than 0.8 in almost all the 26 global land regions and the model bias for at least oneof the cumulus convection schemes is within the discrepancy between observations in about half of theregions.
When ranking the score and absolute error for thesimulated land precipitation in individual regions,
MRI-AGCM3.2 ranks in the top 15 of all 55 modelruns including all 50 of the CMIP5 ensemble members and their mean (except for HPKF for the error
in the annual precipitation). MRI-AGCM3.2 is ranked
almost equal to or higher than the CMIP5 ensemble
mean for the skill scores in the annual and theIto et al. Progress in Earth and Planetary Science(2020) 7:77

seasonal mean and the biases in the seasonal mean.
Therefore, MRI-AGCM3.2 can represent the spatial
pattern and the regional mean of precipitation realistically in more regions than the CMIP5 ensemble
members.
The spatial distribution of projected change in global
precipitation of the ensemble means of MRI-AGCM3.2
and the CMIP5 models is similar. MRI-AGCM3.2 projects a greater increase of regional-mean precipitation
than the CMIP5 ensemble mean, from 0.5 to 2% greater
around the Tibetan Plateau and East Asia and even more
in Australia where the model bias is small in MRIAGCM3.2. This is also the case for the comparison withthe high-performing CMIP5 ensemble.
The sign of future change for annual precipitationis the same at the 50th percentile in 20 out of 26 regions and the opposite in the other six regions
around the tropical South Pacific; e.g., Australia andSouth America. In these six regions, the skill score
for the present-day precipitation exceeds 0.8 in three
regions and the bias is comparable to the difference
between the observations in four regions. Thus, we
expect that the use of the prescribed SST in the
MRI-AGCM3.2 simulations results in a small bias in
the present-day regional precipitation, while the difference between models with or without atmosphere–
ocean coupling has an influence on the future projections. Although the relationship between the ability toreproduce the present-day climate and the ability toprovide reliable future predictions is still unclear (e.g.,Smith and Chandler 2010; He and Soden 2016), highperformance in simulations of the present-day precipitation by MRI-AGCM3.2 is an important metricfor assessing the accuracy of the model itself.
The uncertainty in the MRI-AGCM3.2 projections
partly overlaps the maximum–minimum range of theuncertainty in the projections with the CMIP5 model
ensemble in all regions. By considering the uncertainty range from MRI-AGCM3.2 as a fraction of therange from the CMIP5 models, the percentage of gridcells with the fraction between 0.7 and 1.3 (between
0.5 and 1.5) is approximately 10% (35%). The percentage increases to 45% (75%) when the comparison ismade with respect to the CMIP5 models that perform
well for precipitation simulations in the present-day,suggesting that the uncertainty is closer to that fromthe high-performing CMIP5 ensemble than that fromthe full ensemble. Also, regarding the uncertainty infuture changes in the regional mean precipitation, therange from the MRI-AGCM3.2 projections increases
from 0.4 of the range from all the CMIP5 models toaround 0.8 of that from the high-performing CMIP5
models on average over individual regions. That is tosay, the MRI-AGCM3.2 dataset provides the regional

Page 18 of 20

information on the projections with an uncertainty
comparable to the uncertainty from the highperforming CMIP5 models around the globe, together
with well-represented precipitation in the present-day
climate.
The difference of projections by MRI-AGCM3.2 relative to the CMIP5 model ensemble may be caused partly
by the difference between an atmospheric model and theatmosphere–ocean coupled models of the CMIP5AOGCMs, as seen in regions around the tropical Pacific.
MRI has developed a new model system that assimilates
observed SST in such coupled models, which enables
the evaluation of atmosphere–ocean coupled effects; e.g.,
SST and cloud–radiation feedback (Kitoh and Arakawa
1999). Comparisons of projections from such a model
will facilitate investigation of the causes of the differences identified here.
The characteristics of the precipitation climatology
summarized in this study are highly useful not only
for studies of global and regional climatology using
the MRI-AGCM3.2 dataset but also for decisions on
adaptation strategies. As already discussed on the
CORDEX phase 1 in Ito et al. (2020a), the use of a
GCM dataset to discuss regional climate changes requires a fundamental evaluation of the projections by
the GCM as the regional scale, as we have done here.
This will be applied, for example, with HighResMIP
and CORDEX phase 2.

6 Supplementary Information
The online version contains supplementary material available at https://doi.
org/10.1186/s40645-020-00394-4.
Additional file 1: Table S1. CMIP5 models used in this study.
(Retrieved from https://cmip.llnl.gov/cmip5/availability.html on May 23,2019). Figure S1. As in Fig. 4 but for (a) JJA mean precipitation and (b)DJF mean precipitation. Figure S2. As in Fig. 5 but for (a) JJA mean
precipitation and (b) DJF mean precipitation (%). Figure S3. As in Fig. 6
but for the AMIP-type simulations instead of the CMIP5 simulations. Black
number corresponds to the number in the list of models at the bottom.
Gray number denotes the rank in Fig. 6 only for the model of which thename coincides with that in Table S1. Figure S4. As in Fig. 7 but for JJA
mean precipitation (% K–1). White in (a–d) and hatching in (e–j) indicate
the grid cells where the models simulate no precipitation in the presentday climate. Figure S5. As in Fig. 7 but for DJF mean precipitation (% K–
1). White in (a–d) and hatching in (e–j) indicate the grid cells where themodels simulate no precipitation in the present-day climate. Figure S6.
As in Fig. 8 but for (a) JJA mean precipitation and (b) DJF mean precipitation. Hatching indicates the grid cells where the models simulate no precipitation in the present-day climate. Figure S7. As in Fig. 9 but for (a)JJA mean precipitation and (b) DJF mean precipitation (% K–1). Figure
S8. Future change anomaly for each of 12 HF ensemble members fromtheir mean. (a) Future change of geopotential height at 500 hPa and (b)that of sea level pressure. Members on each row are the member withthe same SST-change pattern for the future climate simulation. Members
on each column are the member with the same cumulus convection
scheme. The anomaly is normalized by the annual variation of individual
variables in the present-day climate. Crosshatching (Hatching) indicatesthe 99% (90%) confidence level. Figure S9. Standard deviation in futurechanges of sea level pressure using three clusters of the CMIP5 AOGCMs

Ito et al. Progress in Earth and Planetary Science(2020) 7:77

and all of them, which were created to obtain future change patterns ofsea surface temperature and sea ice for the MRI-AGCM3.2 simulations by
Mizuta et al. (2014). The deviation is normalized by the annual variationin the present-day climate averaged over the CMIP5 models in each cluster. (see text for details).

Abbreviations
GCM: General circulation model; MRI-AGCM3.2: Meteorological ResearchInstitute atmospheric GCM version 3.2; CMIP5: Coupled Model
Intercomparison Project phase 5; AOGCM: Atmosphere–ocean coupled GCM;
RCM: Regional climate model; IPCC AR5: The Fifth Assessment Report of theIntergovernmental Panel on Climate Change; CORDEX: The CoordinatedRegional Downscaling Experiment; HighResMIP: The High Resolution Model
Intercomparison Project; SST: Sea surface temperature; KF: Kain–Fritsch;
AS: Arakawa–Schubert; YS: Yoshimura; HadISST: Hadley Centre’s sea ice andsea surface temperature dataset; RCP: Representative concentration pathway;
JJA: June–August; DJF: December–February; IPCC SREX: The IPCC Special
Report on Managing the Risks of Extreme Events and Disasters to Advance
Climate Change Adaptation; MAE: Mean absolute error; GPCC: Global
Precipitation Climatology Centre; CPC: Climate Prediction Center; CRU
TS: Climatic Research Unit Timeseries; PRECL: Precipitation Reconstruction
Over Land; CMAP: CPC Merged Analysis of Precipitation; GPCP-1DD: Global
Precipitation Climatology Project 1 degree daily; MSWEP: Multi-source
weighted-ensemble precipitation; MME: Multi-model ensemble; FR: Fractionalof a relative range; ENSO: El Niño/Southern Oscillation
Acknowledgements
The authors would like to acknowledge Dr. H. Endo, Dr. H. Kawai, and Dr. Y.
Imada of the Meteorological Research Institute, Japan meteorological
Agency, and Dr. H. Shiogama of the National Institute for Environmental
Studies for their constructive comments and useful suggestions. In addition,
the authors would like to appreciate the anonymous reviewers who
provided insightful comments on the earlier version of this paper.
Authors’ contributions
All authors conceptualized the study and participated in the discussion. RI
analyzed the data, prepared and revised the manuscript. All authors
approved the final manuscript.
Funding
This work was supported by the Integrated Research Program for Advancing
Climate Models (TOUGOU) Grant Number JPMXD0717935561 from the
Ministry of Education, Culture, Sports, Science and Technology (MEXT), Japan.
Availability of data and materials
CMIP5 multimodel dataset is publicly available via the website of Earth
System Grid Federation (http://pcmdi9.llnl.gov/). Observation products are
publicly available online via each website: GPCC (https://www.dwd.de/EN/
ourservices/gpcc/gpcc.html), CPC (https://ftp.cpc.ncep.noaa.gov/precip/CPC_
UNI_PRCP/), CRU (https://crudata.uea.ac.uk/cru/data/hrg/cru_ts_4.01/), PRECL(http://ftp.cpc.ncep.noaa.gov/precip/50yr/gauge/), CMAP (https://ftp-cpc.
ncep.noaa.gov/precip/cmap), GPCP (ftp://meso.gsfc.nasa.gov/pub/gpcp-v2.2/), MSWEP (http://www.gloh2o.org). The availability of the dataset created by
MRI-AGCM3.2 is limited due to the property of TOUGOU program, MEXT,
Japan, but are available from the corresponding author on reasonable
request or under the condition of collaborative research.
Competing interests
The authors declare that they have no competing interest.
Received: 27 February 2020 Accepted: 24 November 2020

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