8SOLA, 2019, Vol. 15A, 8−12, doi:10.2151/sola.15A-002
©The Author(s) 2019. This is an open access article published by the Meteorological Society of Japan under a Creative Commons
Attribution 4.0 International (CC BY 4.0) license (http://creativecommons.org/license/by/4.0).
The high temperature event in July 2018 caused record-break-
ing human damage throughout Japan. Large-ensemble historical
simulations with a high-resolution atmospheric general circulation
model showed that the occurrence rate of this event under the
condition of external forcings in July 2018 was approximately
20%. This high probability was a result of the high-pressure
systems both in the upper and lower troposphere in July 2018.
The event attribution approach based on the large-ensemble
simulations with and without human-induced climate change
indicated the following: (1) The event would never have happened
without anthropogenic global warming. (2) The strength of the
two-tiered high-pressure systems was also at an extreme level and
at least doubled the level of event probability, which was inde-
pendent of global warming. Moreover, a set of the large-ensemble
dynamically downscaled outputs revealed that the mean annual
occurrence of extremely hot days in Japan will be expected to
increase by 1.8 times under a global warming level of 2°C above
(Citation: Imada, Y., M. Watanabe, H. Kawase, H. Shiogama,
and M. Arai, 2019: The July 2018 high temperature event in Japan
could not have happened without human-induced global warming.
SOLA, 15A, 8−12, doi:10.2151/sola.15A-002.)
In July 2018, Japan experienced extremely high temperature.
The accumulated number of weather stations reporting extremely
high daily maximum temperatures (exceeding 35°C) was more
than 6000 based on the 927 stations. This heat event caused
damage to human health with 1032 deaths during this period (based
on the statistical summary provided by the Japanese Ministry
of Health, Labor and Welfare). Human-induced global warming
probably contributed to the high temperatures in Japan and other
East Asian regions in recent decades (Imada et al. 2014; Imada
et al. 2017). Moreover, two seasonal high-pressure systems,
namely the North Pacic subtropical high (NPSH) in the lower
troposphere and Tibetan high in the upper troposphere often
cause warm climate in Japan (Imada et al. 2014). This two-tiered
high-pressure system (double-High) was also visible in July 2018
(Figs. 1a and b). Shimpo et al. (2019) stated that the expansion of
the Tibetan High to Japan was attributable to the repeated mean-
dering of the subtropical jet stream (STJ), and expansion of the
NPSH was attributable to the meridional dipole pressure pattern
(the Pacic-Japan (PJ) pattern; Nitta 1987; Kosaka and Nakamura
2010) associated with enhanced convective activity around the
Philippines. The meandering of the polar front jet (PFJ) also
contributed to the development of the double-High in July 2018.
However, experts do not have any answers on whether the dou-
ble-High in 2018 was extreme compared to other such historical
events. It is even more difcult to determine the extent to which
human-induced global warming contributed to this event.
Attributing changes in the probability of a specic extreme
event to possible causes is called event attribution (EA; Stott
2016). A set of large-ensemble model simulations under condi-
tions with and without human-induced climate change is often
utilized as an effective tool of EA. In this study, we quantitatively
estimated the contribution of both human-induced global warming
and the double-High toward the July 2018 high temperature event
in Japan using high-resolution large-ensemble historical and non-
warming simulations, the so-called d4PDF (Mizuta et al. 2017).
We also estimated the occurrence of extremely hot days at each
grid points in Japan owing to global warming in the near future.
2. Observational data set and large ensemble simu-
The data for tropospheric temperatures and geopotential
heights was obtained from the 55-year Japanese reanalysis (JRA-
55) dataset (Kobayashi et al. 2015). For the accumulated number
of stations reporting extremely high temperature, we used the data
source provided by JMA which is based on the automated meteo-
rological data acquisition system (AMeDAS; approximately 927
stations at intervals of approximately 21 km) maintained by the
Our large-ensemble pairs of global and regional climate model
simulations are part of the database for policy decision-making for
future climate change (d4PDF) (Mizuta et al. 2017). The global
climate simulations were conducted using the atmospheric general
circulation model (MRI-AGCM3.2) developed by the Meteoro-
logical Research Institute, with a grid spacing of approximately
60 km (Mizuta et al. 2012). The regional climate simulations
were conducted using the nonhydrostatic regional climate model
(NHRCM) with a 20 km grid spacing (Sasaki et al. 2008). The
regional climate simulation covered the East Asian region.
The historical simulations (HIST) were forced by the histor-
ical sea surface temperature (SST) and sea ice thickness/concen-
tration based on COBE-SST2 (Hirahara et al. 2014) and historical
anthropogenic and natural forcing agents such as greenhouse
gases and solar irradiance; the RCP4.5 emission scenario was
used for the period after 2006, comprising a 100-member ensem-
ble with different initial conditions and SST perturbations from
1951 to the present. The non-warming simulations (non-W) were
forced by the historical natural forcing agents and counterfactual
“natural” SST and sea ice estimated by removing the warming
trends observed in the 20th century. The anthropogenic forcings
were xed at a value of 1850 in the non-W (for more details, refer
to Shiogama et al. 2016). In the ofcial d4PDF, the HIST/non-W
datasets of MRI-AGCM3.1 (60km resolution) cover the period
from 1951 to 2010. For the regional simulations, only the HIST
ensemble of NHRCM (20 km resolution) is available. Thus, we
extended the calculations to July 2018 and added the downscaled
calculations as a quasi-real-time product.
In this paper, the model analyses presented in Sections 3 and 4
are based on the 60 km global model. The 20 km regional climate
simulations are used in Section 5 to estimate the simulated total
area of locations experiencing extremely hot days.
The July 2018 High Temperature Event in Japan Could Not Have Happened
without Human-Induced Global Warming
Yukiko Imada1, Masahiro Watanabe2, Hiroaki Kawase1,
Hideo Shiogama3, and Miki Arai2
1Meteorological Research Institute, Japan Meteorological Agency, Ibaraki, Japan
2Atmosphere and Ocean Research Institute, University of Tokyo, Chiba, Japan
3National Institute for Environmental Studies, Ibaraki, Japan
Corresponding author: Yukiko Imada, Meteorological Research Institute,
Japan Meteorological Agency, 1-1 Nagamine, Tsukuba, Ibaraki 305-0052,
Japan. E-mail: email@example.com.
9Imada et al., Event Attribution of the July 2018 High Temperature Event
dominant intrinsic mode from JRA-55. Here, the area mean inside
each analysis region was removed from Z200 and Z850 to obtain
a relative circulation eld. The rst principle mode (SVD1) shows
the double-High structure (Figs. 2b and 2d). The Z850 eld shows
the typical PJ pattern (Fig. 2b), and the Z200 eld shows the
Rossby wave trains along the STJ and the PFJ causing meandering
of the jet streams. The patterns in Figs. 2b and 2d well represent
the anomaly patterns in July 2018 (Figs. 1a and 1b), indicating
that the SVD1 can capture the combined mode of the PJ pattern
and the Rossby wave trains along the STJ and the PFJ. The
expansion coefcient of July 2018 for Z200 (Z850) was the fourth
highest (the second highest), indicating that the circulation aspect
was also extreme compared to the historical record in JRA-55
(black line in Figs. 2a and 2c), although we have experienced the
same level of double-High events a few times in the past. The
scatter plot of the two expansion coefcients of JRA-55 (black
cross marks in Fig. 2e) also conrms the extremeness of the
double-High in July 2018. Hereafter, we used the two expansion
coefcients as the index of the double-High. The long-term
trend of the double-High index remains at the same level unlike
3. Historical variations in temperature and the two-
tiered high-pressure system
Figure 1e shows historical changes in the lower-troposphere
(850 hPa) temperature (T850) in July averaged over Japan
(130°E−147°E, 30°N−43°N) based on JRA-55, HIST, and non-W.
Here, we used T850 to dene the temperature index in place of
land surface temperature because, in AGCM simulations forced
by the prescribed SST, variance of surface temperature could be
underestimated on small islands due to a direct impact of the sur-
rounding SST. The JRA-55 time series shows unprecedented high
temperatures in July 2018. The HIST ensemble adequately covers
the historical temperature changes over Japan and the ensemble
mean eld reproduces record high temperatures in July 2018.
To identify the double-High events from the historical record,
we performed a singular value decomposition (SVD) analysis
using the upper- (200 hPa) and lower- (850 hPa) tropospheric geo-
potential heights (hereafter, Z200 and Z850, respectively) around
Japan (See rectangles in Figs. 2b and 2d) to obtain a concurrent
Fig. 1. Anomalies in July 2018 for (a) Z850 [m] according to JRA-55, (b) Z200 [m] according to JRA-55, (c) Z850 [m] according to HIST, and (d) Z200
[m] according to HIST. 100-member ensemble-mean anomalies are shown in (c) and (d). (e) Time series of T850 [K] averaged in 130°E−147°E and 30°N−
43°N. Black: JRA-55; red: ensemble mean of HIST; blue: ensemble mean of non-W. The shadings indicate the range of all members.
10 SOLA, 2019, Vol. 15A, 8−12, doi:10.2151/sola.15A-002
the warming trend presented in Fig. 1e. Thus, the double-High
condition appears to be natural variability and not affected by the
human-induced climate change at this stage.
The double-High indices for the model simulations were
estimated by projecting each member onto each singular vector
obtained from the JRA-55 records. The obtained indices are
plotted in Figs. 2a, 2c, and 2e. The ensemble means of the Z200
and Z850 indices both for HIST and non-W in July 2018 also
show positive peaks (Figs. 2a and 2c). The scatter plot of the July
2018 samples (squares in Fig. 2e) also shifts to the rst quadrant.
These features are consistent with the fact that the ensemble-mean
spatial anomaly patterns in Figs. 1c and 1d show the development
of a double-High structure similar to that in the reanalysis dataset
over Japan in July 2018 although the wave trains along the STJ
and the PFJ are not clear in the model. Moreover, the magnitude
of the ensemble-mean signals is smaller than the amplitude of
anomalies in the reanalysis. This implies that the double-High
pressure system in July 2018 was not purely because of the sto-
chastic atmospheric internal variability but was partly forced by
the SST, sea ice, or any other external forcing. The weak La Niña
condition in July 2018 could have enhanced convection around
the Philippine Sea and the development of the NPSH (Kosaka
et al. 2013). The La Niña event in the preceding winter could also
have a role in developing the PJ pattern in the summer through the
tropical Indian Ocean warming (Xie et al. 2009; Hu et al. 2011).
On the other hand, the source of the Rossby-wave propagations
along the STJ and the PFJ are not easy to detect. These points will
be discussed in another paper.
Again, the difference in the double-High indices between
HIST and non-W is unclear (the red and blue lines in Figs. 2a and
2c), indicating that the impact of anthropogenic climate change
on the double-High event is little. Note that, the results do not
change when the singular vectors obtained from the SVD analysis
of HIST and non-W are used to dene the double-High index
although the contribution rates of SVD1 based on the simulations
are slightly smaller than that based on JRA-55.
Fig. 2. SVD1 expansion coefcients normalized by each standard deviation of JRA-55 for (a) Z200 and (c) Z850 from the SVD analysis based on JRA-
55 (non-dimensional). Shadings indicate the range of normalized expansion coefcients for simulations estimated by projecting each member onto each
singular vector obtained from the JRA-55 records. (b) and (d) Z200 and Z850 anomaly patterns of JRA-55 regressed onto each expansion coefcient ([m],
black lines of a and c), respectively. The stippling in (b) and (d) indicates the area exceeding 99% signicance level by t-test. (e) Scatter plot of the SVD1
expansion coefcient for Z850 against the coefcient for Z200. The red (blue) squares denote the coefcients of HIST (non-W) in July 2018, and the dark
yellow (light-blue) circles denote the coefcients of HIST (non-W) from 1958 to 2017. The black cross marks denote the coefcients of JRA-55 from 1958
to 2018 (The value of 2018 is highlighted).
11Imada et al., Event Attribution of the July 2018 High Temperature Event
4. Event Attribution study
In this section, we focus on the event probability in July
2018. Figure 3 shows the probability density functions (PDFs) of
the T850 indices dened in Fig. 1. The difference in the curves
for HIST (red) and non-W (blue) indicates the impact of human
activity. The probability of exceeding the level of the July 2018
temperature (T850 = 18.5°C) is 19.9% (a condence interval
is 15.5−24.5% by 10th and 90th percentiles of 5000 bootstrap
random sampling) for HIST, while it is 3.31 × 10−5% (1.13 × 10−5−
5.69 × 10−5%) for non-W. This result indicates that the warm event
during July 2018 would never have happened without anthro-
pogenic global warming. Moreover, when we considered only
the subsample of HIST with a double-High system where both
the expansion coefcients are positive (the rst quadrant of Fig.
2e) in the SVD analysis presented in Section 3, the probability
increased to 24.6% (20.0−29.4%) as shown in Fig. 3 with the
magenta-dashed curve). On the other hand, when we considered
only the subsample without a double-High system where both or
one of the expansion coefcients are negative (the second, third,
and forth quadrant of Fig. 2e), the probability reduced to 12.2%
(7.62−17.1%) as shown with the orange-dashed curve in Fig.
3. Note that, a case “with” a double-High dened here does not
necessarily correspond to a case as strong as the double-High in
July 2018 but includes weaker double-High systems. Therefore,
the double-High structure also had an important role in at least
doubling the risk of the 2018 high temperature event, which is
independent of anthropogenic impacts.
5. Occurrence of extremely hot days at each grid
points in Japan
Using large-ensemble high-resolution (20 km mesh) regional
products, we can estimate the impact of human-induced global
warming to regional-scale extreme events. As an example, Fig. 4a
presents the difference in the number of extremely hot days in July
2018 between HIST and non-W at each grid point of the regional
model. Here, the JMA dened the threshold for extremely high
temperatures as 35.0°C, which is approximately the 99-percentile
value of the total daily maximum temperature observations during
2010−2017. For the model simulations, we used the 99-percentile
value of HIST in the same period (32.9°C) as the threshold
because the simulated grid-mean temperature underestimated the
temperature observed at the sites. Figure 4a shows a pronounced
increase in the extremely hot days in the populated areas of Japan
during July 2018 due to the anthropogenic climate change. Further
details of the regional aspects of the July 2018 warm event will be
presented in another paper.
The large-ensemble regional products are also available to
project near-future changes in regional extremes. We presented
the density of the scatter plot of the mean annual occurrence of
extremely high temperatures (vertical axis) relative to the global
annual-mean surface air temperature (horizontal axis) using
the HIST and non-W simulations (Fig. 4b). The mean annual
occurrence (unit: days/year/grid) was estimated as accumulated
fraction of grid points experiencing an extremely high daily
maximum temperature within each year among all the grid points
in Japan (1350 grid points) throughout each year. For reference,
mean annual occurrence estimated from the AMeDAS sites (total
927 sites) and global-mean surface temperature provided by the
JMA were also plotted for the period after 2010. The white curve
indicates the cumulative distribution function (CDF) of the daily
maximum temperatures in Japan for all the grid points and days of
non-W from 1951 to 2017 in assuming that the PDF of daily max-
Fig. 4. (a) Difference in the number of extremely hot days in July 2018 between HIST and non-W at each grid point of the regional model [days/month].
Ensemble-mean values are plotted. (b) Density of the scatter plot [counts/bin] of the mean annual occurrence [days per year] of extremely high daily max-
imum temperature per the total number of grid points of Japan against the global annual-mean surface air temperature [K]. We divided the plot area into
1750 bins; each bin is dened as a space with 0.06 K (horizontal) and 0.2 days/year/grid (vertical). Warm-color (cold-color) contours are results from all
members and years (1958−2017; the results of 2018 are not plotted because the simulations ended at July 2018) from the HIST (non-W) run with an inter-
val of 20 (40) counts per bin starting at 1. For reference, the mean annual occurrence estimated from the AMeDAS sites after 2010 are also plotted, indicat-
ed by open-black squares. The white curve indicates the CDF estimated from surface temperatures in Japan for all grid points and days of the non-W run
from 1951 to 2017, and the gray curves indicate 5000 CDFs generated by the bootstrap resampling.
Fig. 3. PDFs for T850 [K] over Japan. The red- (blue-) solid line is the
result of the July 2018 ensemble simulations of HIST (non-W). The
magenta-dashed and orange-dashed line was estimated from the members
with and without the double-High system in the HIST run, respectively.
12 SOLA, 2019, Vol. 15A, 8−12, doi:10.2151/sola.15A-002
imum temperature for all grid points of Japan shifts in a positive
direction in proportion to the global-mean surface temperature
with a xed shape. The CDF curve well represents the combined
plots for HIST and non-W, indicating an average level of the mean
annual occurrence of extremely high temperatures per year at each
warming stage. Since the last decade, the warming level across
the world has been at approximately 1°C. Thus, in recent years,
it is not surprising that the mean annual occurrence of high tem-
peratures is equal to almost 2.7 days per year per grid. Again, the
extremely high temperature event in 2018 was an extraordinary
situation in which extreme temperatures were experienced almost
seven days per year per sites of AMeDAS on average.
Using the CDF curve shown in Fig. 4b, we could project near-
future trends regarding the mean annual occurrence of high
temperatures. Under a global warming level of 1.5°C (2°C), the
average days that could experience high temperatures is estimated
to be approximately 3.6 (4.8) days/year/grid; such a fraction was
observed only a few times in the past.
6. Discussion and conclusion
This paper presented a quantitative estimate of the contribu-
tion of human-induced climate change to the extremely high tem-
perature experienced in Japan in July 2018 using a large-ensemble
database, the so-called d4PDF. By comparing the event probabil-
ities between the historical (realistic) and non-warming (without
human impact) ensemble sets, we concluded that the warm event
in July 2018 would never have happened without human-induced
Furthermore, this is an initial attempt to quantify not only the
human-induced effect but also the contribution of specic natural
variabilities. In the July 2018 case, the double-High system was
the main natural variability, which is independent of anthropo-
genic effects. We separated the 2018 simulations into members
with and without double-High development and found that the
probability of extremely high temperatures at least doubled owing
to the existence of the double-High pressure system in July 2018.
The July 2018 high temperature event caused serious damage
to human lives in Japan. We have the responsibility of reporting
to what extent this disastrous event was attributable to human
activity to provide concrete results and alert people to take all
possible steps to minimize future damage associated with anthro-
pogenic global warming. To emphasize this fact, we also projected
near-future trends of the mean annual occurrence of extremely hot
days in Japan. Our results suggested that the extremely high tem-
peratures experienced only a few times in the past could become a
usual situation with warming levels of 1.5 or 2°C in the next few
Note that, model dependency and uncertainty in the boundary
conditions of non-W are big issues in an EA research. There is
an international multi-model intercomparison project for EA to
address these issues (Stone 2013). Although the EA simulations of
d4PDF has a higher resolution and a longer-term coverage of peri-
ods than the other state-of-the-art EA databases in the world, the
weak point is in consideration of the uncertainties. Huge compu-
tational costs limit the goal. The results in this paper appears to be
the best estimates within our current capabilities. The remaining
issues will be explored in the future.
This is a prompt report, and we skipped the discussion regard-
ing how the development of the double-High system (that is, the
PJ pattern and the Rossby wave-like patterns) in July 2018 was
forced by large-scale factors. The specic SST in 2018 possibly
had a role in forming the double-High system. Further discussions
regarding this issue will be reported in future papers.
We are grateful to M. Mori, and C. Takahashi for their coop-
eration. This work was supported by the TOUGOU program of
the Japanese Ministry of Education, Culture, Sports, Science and
Technology (MEXT). This study utilized the d4PDF, which was
produced using the Earth Simulator as “Strategic Project with
Special Support” of JAMSTEC under corporations among the
programs of SOUSEI, TOUGOU, and SI-CAT, which all were
sponsored by the MEXT, Japan. This study was partly supported
by Japan Science and Technology Agency (JST) and Japan Society
for the Promotion of Science (JSPS) Grants-in-Aid for Scientic
Research (KAKENHI) Grant Numbers 18K19951 and 18K03749.
Edited by: Y. Kosaka
Hirahara, S., M. Ishii, and Y. Fukuda, 2014: Centennial-scale sea
surface temperature analysis and its uncertainty. J. Climate, 27,
Hu, K., G. Huang, and R. Huang, 2011: The Impact of tropical Indian
Ocean variability on summer surface air temperature in China. J.
Climate, 24, 5365−5377.
Imada, Y., and co-authors, 2014: The contribution of anthropogenic
forcing to the Japanese heat waves of 2013. Bull. Amer. Meteor.
Soc., 95, S52−S54.
Imada, Y., and co-authors, 2017: Climate change increased the like-
lihood of the 2016 heat extremes in Asia. Bull. Amer. Meteor.
Soc., 97, S97−S101.
Kobayashi, S., and co-authors, 2015: The JRA-55 Reanalysis: General
specications and basic characteristics. J. Meteor. Soc. Japan,
Kosaka, Y., and H. Nakamura, 2010: Mechanisms of meridional tele-
connection observed between a summer monsoon system and
a subtropical anticyclone. Part I: The Pacic-Japan pattern. J.
Climate, 23, 5085−5108.
Kosaka, Y., S.-P. Xie, N.-C. Lau, and G. A. Vecchi, 2013: Origin of
seasonal predictability for summer climate over the Northwest-
ern Pacic. PNAS, 110, 7574−7579.
Mizuta, R., H. Yoshimura, H. Murakami, M. Matsueda, H. Endo,
T. Ose, and A. Kitoh, 2012: Climate simulations using MRI-
AGCM3.2 with 20-km grid. J. Meteor. Soc. Japan, 90A,
Mizuta, R., and co-authors, 2017: Over 5000 years of ensemble future
climate simulations by 60 km global and 20 km regional atmo-
spheric models. Bull. Amer. Meteor. Soc., 98, 1383−1398.
Nitta, T., 1987: Convective activities in the tropical western Pacic
and their impact on the Northern Hemisphere summer circula-
tion. J. Meteor. Soc. Japan, 65, 373−390.
Sasaki, H., K. Kurihara, I. Takayabu, and T. Uchiyama, 2008: Prelim-
inary experiments of reproducing the present climate using the
non-hydrostatic regional climate model. SOLA, 4, 25−28.
Shimpo, A., K. Takemura, S. Wakamatsu, H. Togawa, Y. Mochi zuki,
M. Takekawa, S. Tanaka, K. Yamashita, S. Maeda, R. Kurora, H.
Murai, N. Kitabatake, H. Tsuguti, H. Mukougawa, T. Iwasaki, R.
Kawamura, M. Kimoto, I. Takayabu, Y. Takayabu, Y. Tanimoto,
T. Hirooka, Y. Masumoto, M. Watanabe, K. Tsuboki, and H.
Nakamura, 2019: Primary factors behind the Heavy Rain Event
of July 2018 and the subsequent heat wave in Japan. SOLA,
15A, in press, doi:10.2151/sola.15A-003.
Shiogama, H., and co-authors, 2016: Attributing historical changes in
probabilities of record-breaking daily temperature and precipi-
tation extreme events. SOLA, 12, 225−231.
Stone, D. A., 2013: Boundary conditions for the C20C Detection and
Attribution project: The ALL-Hist/est1 and NAT-Hist/CMIP5-
est1 scenarios. International CLIVAR C20C+ Detection and
Attribution Project, 18pp. (Available online at https://portal.
Stott, P. A., 2016: Attribution of extreme weather and climate-related
events. Wiley Interdisciplinary Reviews: Climate Change, 7,
Xie, S. P., K. M. Hu, J. Hafner, H. Tokinaga, Y. Du, G. Huang, and T.
Sampe, 2009: Indian Ocean capacitor effect on Indo-western
Pacic climate during the summer following El Niño. J. Cli-
mate, 22, 730−747.
Manuscript received 22 February 2019, accepted 23 April 2019