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Article

Influence of the South Asian High and Western Pacific Subtropical High Pressure Systems on the Risk of Heat Stroke in Japan

by
Takehiro Morioka
1,*,
Kenta Tamura
2 and
Tomonori Sato
3
1
Graduate School of Environmental Science, Hokkaido University, Sapporo 060-0810, Japan
2
Snow and Ice Research Center, National Research Institute for Earth Science and Disaster Resilience, Nagaoka 940-0821, Japan
3
Faculty of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, Japan
*
Author to whom correspondence should be addressed.
Atmosphere 2025, 16(6), 693; https://doi.org/10.3390/atmos16060693 (registering DOI)
Submission received: 15 April 2025 / Revised: 4 June 2025 / Accepted: 5 June 2025 / Published: 8 June 2025
(This article belongs to the Special Issue Weather and Climate Extremes: Past, Current and Future)
Browse Figures
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Abstract

:
Weather patterns substantially influence extreme weathers in Japan. Extreme high temperature events can cause serious health problems, including heat stroke. Therefore, understanding weather patterns, along with their impacts on human health, is critically important for developing effective public health measures. This study examines the impact of weather patterns on heat stroke risk, focusing on a two-tiered high-pressure system (DH: double high) consisting of a lower tropospheric western Pacific subtropical high (WPSH) and an overlapping upper tropospheric South Asian high (SAH), which is thought to cause high-temperature events in Japan. In this study, the self-organizing map technique was utilized to investigate the relationship between pressure patterns and the number of heat stroke patients in four populous cities. The study period covers July and August from 2008 to 2021. The results show that the average number of heat stroke patients in these cities is higher on DH days than on WPSH days in which SAH is absent. The probability of an extremely high daily number of heat stroke patients is more than twice as high on DH days compared to WPSH days. Notably, this result remains true even when WPSH and DH days are compared within the same air temperature range. This is attributable to the higher humidity and stronger solar radiation under DH conditions, which enhances the risk of heat stroke. Large-scale circulation anomalies similar to the Pacific–Japan teleconnection are found on DH days, suggesting that both high humidity and cloudless conditions are among the large-scale features controlled by this teleconnection. Early countermeasures to mitigate heat stroke risk, including advisories for outdoor activities, should be taken when DH-like weather patterns are predicted.

1. Introduction

Extreme high temperature events have increased in association with progressing global warming; this has led to increased risks of heat stroke worldwide [1]. In 2003, more than 70,000 people died in Europe as a result of heat stroke [2]. A persistent heatwave in western Europe in 2022 resulted in a death toll of 61,672 during the period from May 30 to September 4 [3]. It is estimated that, by 2100, 48–74% of the world’s population will be affected by an increasing frequency of extreme high-temperature events [4]. Therefore, the risk of heat stroke is expected to increase in the future [5] and, hence, it is a central human health concern at a global scale.
The risk of developing heat stroke is known to be increased by both intrinsic and extrinsic factors. Intrinsic factors include the illnesses and genetic predispositions of individuals [6,7]. In particular, elderly individuals have a high risk of heat stroke [8,9] because the thermoregulatory function of the human body declines with age [10]. In addition, elderly individuals are more likely to suffer from cardiovascular diseases and diabetes, which increase the risk of severe heat stroke [7]. Therefore, in countries with ageing populations, intrinsic factors may increase the overall risk of developing heat stroke. Extrinsic factors include meteorological conditions and excessive clothing [6]. Of the various external factors, meteorological conditions play a crucial role in the onset of heat stroke. It is widely known that the number of heat stroke patients increases as the air temperature increases [11,12,13]. In particular, the daily maximum temperature is the most representative parameter for the daily variation in the number of heat stroke patients [8,14,15]. This indicates that the risk of heat stroke onset is sensitive to the daytime air temperature. In addition to high temperatures, external factors such as high humidity, high thermal radiation, and low wind speed prevent the elimination of heat generated in the body via metabolic processes, increasing the risk of heat stroke [5,16,17]. Even under the same temperature conditions, the evaporation of human sweat tends to be weaker when the air humidity is higher. Such conditions decrease the latent heat flux from the human body, resulting in inefficient heat release. Consequently, the core body temperature increases, which enhances the risk of heat stroke [18]. In addition, strong solar radiation can reduce both endurance exercise capacity and thermoregulation [19,20]. Accordingly, outdoor activities exposed to strong solar radiation increase the risk of heat stroke onset [21,22]. Conversely, higher wind speed can reduce heat stroke risk [23]. Thus, environments characterized by high temperatures, high humidity, strong solar radiation, and low wind speed increase the risk of developing heat stroke.
The risk of heat stroke in Japan is projected to increase in the future [24,25] largely due to the growing proportion of elderly individuals [26]. Moreover, the summer climate, characterized by high temperatures and high humidity within the dominant East Asian summer monsoon [27], further exacerbates this risk. Daily maximum air temperature during July and August exceeds 30 °C in urban areas (Table 1). Despite being surrounded by oceans, Japan faces a substantial heat stroke risk [28]. Therefore, it is crucial to urgently establish social preparedness to adapt to the warming climate and address the increasing risk of heat stroke [29].
There are two circulation systems observed around Japan during the summer period: the southwesterly East Asian summer monsoon flow and the southeasterly flow surrounding the western Pacific subtropical high (WPSH) (Figure 1a). Both flows transport tropical moist air toward Japan (Figure 1a). The WPSH is a predominant weather system in Japan during July and August [30] and typically exhibits a shallow structure (Figure 1a) with a steady overlying mid-Pacific trough (Figure 1b). The WPSH enhances subsidence over Japan, which reduces cloud cover and increases solar radiation, thereby leading to increased surface air temperature [31]. Therefore, when the WPSH extends toward East Asia, the area surrounding Japan is expected to experience hot and humid conditions. The WPSH potentially affects multiple meteorological factors. For example, the northward extension of the WPSH increases moisture transport toward the Korean peninsula and Japan [32]. It also influences the distribution of the cloud cover, thereby affecting the intensity of solar radiation at the surface [33].
In addition to the WPSH, South Asian High (SAH) often develops in the upper troposphere, centered around the southern side of the Tibetan Plateau. In the hot summer, the lower tropospheric WPSH extends westward while the upper tropospheric SAH extends eastward, forming a two-tiered high-pressure system, or a double high (DH), over Japan [31,34]. The vertical overlap of these high pressure systems strengthens the descending motion of air mass. This air mass undergoes adiabatic compression, contributing to the increase in surface temperature [31,35]. It has been suggested that DH conditions led to the record-breaking high-temperature event in Japan that occurred in July 2018 [36]. During this summer, the monthly air temperature at many stations across mainland Japan exceeded the climatological value by +2 °C. Additionally, the highest daily maximum temperature record in Japan (41.1 °C on 23 July 2018) was observed in Kumagaya, approximately 60 km north of Tokyo [37,38]. Furthermore, the enhanced downward airflow (i.e., subsidence) contributed to reduced cloud cover, increasing solar radiation and subsequently elevating the risk of heat stroke beyond what would be expected from high temperatures alone [37].
As mentioned above, the distributions of the WPSH and SAH, i.e., the weather patterns (WPs) that appear in the lower and upper troposphere, may have combined impacts on the risk of heat stroke through changes in multiple meteorological elements (e.g., air temperature, specific humidity, and solar radiation). However, the role of these WPs in the daily variation of the risk of heat stroke has not previously been investigated. Therefore, the objective of this study was to clarify the influence of WPs on the risk of heat stroke in Japan. The novelty of this study lies in the incorporation of WPs, allowing for the consideration of the co-variability of multiple weather elements. This approach is more advantageous than individually analyzing the elements. This finding may contribute to the development of early preventive measures.

2. Materials and Methods

2.1. Materials

This study examined daily data concerning heat stroke patients transported by ambulance for each prefecture, provided by the Fire Disaster Management Agency [39], and population data derived from the 2021 Population Census, provided by the Statistics Bureau of Japan [40]. This study focused on four prefectures (Tokyo, Osaka, Aichi, and Fukuoka) that include large urban areas with populations of >5 million people. The analyzed period was from 2008 to 2021 and the months of July and August when the number of heat stroke patients consistently exceeded 1000 per month in all four prefectures (Figure 2).
In this study, surface meteorological observation data [41] were used to investigate the meteorological conditions. Because there are several meteorological stations in each prefecture, we calculated representative values for each prefecture by taking spatial average of the observation sites within the prefecture (Figure 3a). For Tokyo, stations located outside the main island were excluded. To identify days with large horizontal extensions of the SAH and WPSH over Japan, we used geopotential height data obtained from the JRA-55 reanalysis [42]. The horizontal grid spacing of JRA-55 is 1.25° × 1.25° with a temporal resolution of 6 h. Details of the JRA-55 reanalysis data used in this study are summarized (Table A1).

2.2. Methods

We focused on the WPSH and DH, which affect the summer climate in Japan, and investigated the differences in the number of heat stroke patients and meteorological conditions. The analysis procedure is outlined in Figure 4. In this study, we used the self-organizing map (SOM) technique to identify strong WPSH conditions in the lower troposphere at 850 hPa and strong SAH conditions in the upper troposphere at 200 hPa. The SOM technique projects high-dimensional data onto a visually comprehensible, two-dimensional map [43]. The application of SOM in meteorology has been growing rapidly in recent years, primarily for the classification of WPs [44,45]. Geopotential height anomalies at 850 hPa and 200 hPa were separately used as input data to generate SOM for each height. This procedure enables independent examination of WPSH and SAH. The analysis period consists of a total of 868 days (from July to August during the period of 2008–2021). Data for heat stroke patients are available only on daily intervals, whereas the temporal interval for the geopotential height data in JRA-55 is 6 h. To address this gap, we only used the JRA-55 reanalysis at 15:00 Japan Standard Time, which is closest to the time at which the daily maximum temperature occurs. For each of the 850 hPa and 200 hPa WPs classified by the SOMs, the geopotential height anomaly relative to the climatology was calculated over the target region (30.0–38.0° N, 130.0–142.0° E; red box in Figure 3b). We selected the strong days for WPSH and SAH expansions into Japan considering this anomaly. Specifically, the top four of nine WPs were defined as strong WPSH and SAH expansions into Japan based on the height anomalies at 850 hPa and 200 hPa, respectively. These top four WPs correspond to approximately 40% of the analyzed days for both 850 hPa and 200 hPa. Figure 5 depicts the classification results for 850 hPa and 200 hPa, with the average height anomalies in the region outlined in Figure 3b. The days classified as the top four WPs represent positive height anomalies over Japan, exhibiting strong WPSH and SAH extensions. To discuss the special condition under which the WPSH and SAH coexist in a vertically overlapping structure [36], we identified double high days (DH days). Specifically, days with a coincidence of strong WPSH (360 days) and SAH (372 days) are categorized as DH days. In addition, we defined western Pacific subtropical high days (WPSH days) as days with the presence of only the WPSH, determined by excluding the DH days from the strong WPSH days. Based on the above procedure, the number of days classified as DH and WPSH days was 180 for each category.

3. Results

3.1. Number of Heat Stroke Patients and Climate Conditions in Japan

This section introduces the number of heat stroke patients and the climate conditions in Japan. The number of heat stroke patients is high in urban areas, reflecting the large population exposure (Figure 6a). The daily maximum temperature from July to August measured by the Automated Meteorological Data Acquisition System (AMeDAS) maintained by the Japan Meteorological Agency (JMA) shows higher temperatures in western and eastern Japan and lower temperatures in northern Japan (Figure 6b). The climatological daily maximum temperature exceeds 30 °C in western and eastern Japan in accordance with the distribution of WPSH during summer (Figure 1a and Figure 6b). During the study period, the average daily maximum air temperature in July and August across four prefectures does not exhibit a statistically significant trend based on Mann–Kendall test.
Figure 7 illustrates the frequency distribution of the daily number of heat stroke patients. On average, there are 54, 47, 46, and 28 patients per day in the Tokyo, Osaka, Aichi, and Fukuoka prefectures, respectively, roughly reflecting their population differences. Extremely high patient numbers corresponding to the 90th and 95th percentile values far exceed the 100 patients per day in Tokyo and Osaka, followed by Aichi. Given similar temperatures among the prefectures, the differences in the patient numbers are largely attributable to the differences in the population exposure (Figure 6b). The relationship between air temperature and the number of heat stroke patients indicates that an increase in the daily maximum temperature tends to be associated with a higher number of heat stroke patients (Figure 7).

3.2. Difference in Air Temperatures Between DH and WPSH Days

Figure 8 shows probability distributions of the daily maximum air temperatures for the DH and WPSH days. In all four prefectures, the probability peaks are shifted toward higher temperatures on DH days compared with WPSH days. The temperature differences between DH and WPSH days in terms of the mode are 0.6 °C (Tokyo), 1.5 °C (Osaka), 1.0 °C (Aichi), and 0.7 °C (Fukuoka). On DH days, the maximum air temperatures tend to be higher than on WPSH days, likely because the overlap between the SAH and the WPSH causes further temperature increases. In other words, the SAH tends to bring high temperatures to all four prefectures. Furthermore, WPSH days have a wider probability distribution than DH days. This may indicate that extended WPSH days include many cloudy days as well as hot summer days (Figure 8).

3.3. Difference in Heat Stroke Patients Between DH and WPSH Days

Figure 9 shows the probability distribution, comparing DH and WPSH days, of daily heat stroke patients in the four prefectures. A higher probability of greater heat stroke patient numbers is observed on DH days than on WPSH days; this is a common feature regardless of the prefecture (Figure 9). The mean number of heat stroke patients is higher on DH days than on WPSH days, being approximately 1.5 times higher in all four prefectures. According to Figure 8 and Figure 9, hot summer days exceeding 30 °C are more likely to occur on DH days than on WPSH days, resulting in a greater number of heat stroke patients on DH days. A further analysis of meteorological effects other than the air temperature is given in Section 3.5.
Here, the characteristics of days with extremely high heat stroke (EHS) patient numbers are discussed. Days are designated EHS when the number of daily heat stroke patients is equal to or above their respective 90th percentile values in each prefecture (Figure 7). Table 2 shows the number of DH and WPSH days and their percentage relative to the total number of summer days. Table 3 shows the occurrence probability of EHS on DH and WPSH days. The occurrence of EHS on DH days is approximately twice that on WPSH days; this is a common result across all four prefectures (Table 3).

3.4. Difference Between DH and WPSH Days Within the Same Temperature Range

Figure 10 shows the relationship, comparing DH and WPSH days, between the daily maximum air temperature and the number of heat stroke patients in the four prefectures. Interestingly, the average number of heat stroke patients differs between the DH and WPSH days, even when compared within the same temperature range (Figure 10a–d). The solid lines in Figure 10 indicate the mean number of patients computed for every 2 °C increase in the daily maximum temperature. Significant differences in heat stroke patient numbers between DH and WPSH days were found in the ranges of 32–34 °C in Tokyo Prefecture, 30–32 °C and 32–34 °C in Osaka Prefecture, and 30–32 °C and 34–36 °C in Aichi Prefecture, while no significant differences were found in Fukuoka Prefecture. In the temperature ranges in which significant differences were observed, meteorological factors other than temperature might have influenced the differences in the number of heat stroke patients.

3.5. Role of Meteorological Factors on Heat Stroke Risk

It is speculated that greater heat stroke patient numbers on DH days are attributable to anomalous humidity, solar radiation, and wind speed conditions. Figure 11 shows the differences in the humidity, solar radiation, and wind speed between DH and WPSH days for the temperature zones at which significant differences were found in Figure 10. The humidity tends to be higher on DH days than on WPSH days throughout Japan (Figure 11a–c). In terms of the solar radiation, DH days have higher values than WPSH days, with significant differences observed at all targeted prefectures (Figure 11d–f). The wind speed in DH days is slightly higher than WPSH days over the targeted prefectures (Figure 11g–i), which may mitigate the risk of heat stroke in DH days. However, since the number of heat stroke patients on DH days is higher than on WPSH days, it is speculated that the contributions of humidity and solar radiation to heat stroke risk are predominant compared to wind speed. Based on the above assessments, it can be concluded that the risk of heat stroke is greater on DH days than on WPSH days because of the higher humidity and solar radiation. These results remain similar even if the periods of the request-based lockdown due to the COVID-19 pandemic (2020 and 2021) are excluded from the analysis (Figure A2, Figure A3 and Figure A4).

4. Discussion

Why do DH days have a higher 2 m specific humidity than WPSH days, as indicated in Section 3.5, even if they are compared within the same temperature range? Figure 12a confirms that, on DH days, surface humid air is widely distributed across Japan. Water vapor flux anomalies relative to the climatology are positive over the entire Japanese archipelago on DH days (Figure 12a,b), whereas the anomalies are relatively smaller on WPSH days (Figure 12c). On DH days, anticlockwise and clockwise circulation anomalies are seen southeast of Taiwan and east of Japan, respectively (Figure 12a,b). These circulation anomalies enhance southeasterly winds blowing toward southwestern Japan and the Korean peninsula, which facilitates water vapor transport to these regions [46,47]. The strong southwesterly moisture flux over the Sea of Japan transports humid air further north. These differences, in terms of moisture transport, might be a reason behind the higher humidity on DH days compared with WPSH days. These circulation patterns in DH days are similar to what has been observed in association with the Pacific–Japan (PJ) pattern [48], in which active convection over the Philippines enhances the extension of the WPSH toward Japan. Therefore, studies examining the relationship between tropical convection and gyre-like circulation pairs around Japan are necessary.
The upper tropospheric westerly wind over the region from northeastern China to the Sea of Okhotsk is stronger on DH days than on WPSH days; however, it is weak over Japan (Figure 12d). The latitude–height cross sections of the zonal wind at 140° E for DH (Figure 12e) and WPSH (Figure 12f) days indicate that, while the subtropical westerly jet on WPSH days is found at a similar or slightly southerly location relative to the climatological position, a northward shift in the subtropical jet is obvious on DH days. This is consistent with the fact that, on DH days, the geopotential height over Japan is higher in both the lower and upper troposphere. Such height anomalies lead to an increased air thickness over Japan and, thus, cause a northward shift in the westerly jet according to the thermal wind relation. Although there might be other mechanisms that could shift the subtropical jet to north, the PJ pattern is known to promote such a northward shift [49]. The PJ pattern could therefore be one possible factor attributable to a high risk of heat stroke.
Finally, the reason why solar radiation from western Japan to northern Japan is stronger on DH days than on WPSH days is discussed (Figure 11d–f). Figure 13a shows that stronger downward shortwave radiation on DH days is a common feature in western and eastern Japan. It is speculated that such strong solar radiation and high humidity (Figure 12a) lead to an elevated risk of heat stroke. The latitude–height cross section at 140° E shows that the vertical pressure velocities in the upper troposphere are positive (i.e., downward motion) over Japan (30–35° N) on DH days, whereas negative pressure velocity, meaning upward motion, exists above 700 hPa on WPSH days (Figure 13b,c). The difference in the vertical velocity between DH and WPSH days confirms the existence of enhanced subsidence in the lower and upper troposphere over Japan (Figure 13d), confirming that subsidence is dominant throughout the troposphere on DH days. A similar enhancement of subsidence was also observed during the extreme heat event in China in the summer of 2020 [50]. This vertical circulation prevents cloud formation, such that the solar radiation is higher on DH days than on WPSH days.

5. Conclusions

In this study, we investigated the relationship between the number of heat stroke patients and WPs. The results indicate that the probability of high daily maximum air temperatures is greater on DH days than on WPSH days, consistent with the fact that the number of heat stroke patients is higher on DH days. These characteristics are seen in all four targeted prefectures (Tokyo, Osaka, Aichi, and Fukuoka). On DH days, not only the air temperature but also other meteorological elements, including the specific humidity and solar radiation, are higher, contributing to an increase in the risk of heat stroke. In other words, the two-tiered high-pressure structure affects multiple meteorological elements, coherently increasing the risk of heat stroke, such that the number of heat stroke patients tends to be high when DH days occur. These results demonstrate the importance of considering WPs when predicting the risk of heat stroke.
Adaptation to the increasing risk of heat stroke has become a globally important issue in recent years as global warming progresses [51]. In cases of high heat stroke events in Europe, it has been reported that DH-like barotropic structures caused extremely high temperatures [52,53]. In these cases, multiple meteorological factors regulated by WPs may have increased the risk of heat stroke. To mitigate the risk, early countermeasures, including the cancellation of outdoor activities, should be advised when WPs connected to high heat stroke risk are predicted. In the USA, the National Weather Service issues heat stroke alerts to citizens in advance of high-temperature predictions [54]. In Japan, heat stroke alerts were introduced in 2021. Incorporation of a WP-based assessment is expected to provide insights to improve alert systems further.

Author Contributions

Conceptualization, T.M. and T.S.; methodology, T.M. and T.S.; software, T.M.; formal analysis, T.M. and K.T.; investigation, T.M.; data curation, T.M.; writing—original draft preparation, T.M.; writing—review and editing, T.M. and T.S.; supervision, T.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Japan Society for the Promotion of Science KAKENHI (Grant Number: JP24H02228), the Arctic Challenge for Sustainability III (ArCS III; JPMXD1720251001) project, and the SENTAN program (JPMXD0722680734). This work was also supported by JST SPRING, Grant Number JPMJSP2119.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original data of heat stroke patients presented in the study are openly available in the website of the Fire Disaster Management Agency in Japan (https://www.fdma.go.jp/disaster/heatstroke/post3.html (accessed on 10 April 2025)). The reanalysis data, JRA-55, is available from the DIAS website (https://search.diasjp.net/ja/dataset/JRA55 (accessed on 10 April 2025)). Daily maximum temperature data can be obtained from the Japan Meteorological Agency website (https://www.data.jma.go.jp/gmd/risk/obsdl/index.php (accessed on 25 December 2023)).

Acknowledgments

The authors would like to express their sincere gratitude to JMA for providing meteorological data and to the Fire Disaster Management Agency in Japan for making available the data on heat stroke patients.

Conflicts of Interest

The author declares no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
WPWeather Pattern
WPSHWestern Pacific Subtropical High
SAHSouth Asian High
DHDouble High
AMeDASAutomated Meteorological Data Acquisition System
JMAJapan Meteorological Agency
SOMSelf-Organizing Map
EHSExtremely high Heat Stroke
PJPacific-Japan

Appendix A

Table A1. Detailed information of the dataset derived from JRA-55 used in this study.
Table A1. Detailed information of the dataset derived from JRA-55 used in this study.
DataPeriodResolutionVariablesSource
JRA-55July and August in 2008–20211.25° × 1.25°geopotential height
2 m specific humidity
surface downward shortwave radiation
10 m wind speed		[42]
Atmosphere 16 00693 g0a1
Figure A1. Differences in number of patients required to satisfy 95% confidence level (blue) and the difference in the average number of patients between DH days and WPSH days (orange) for each bin of daily maximum air temperature.
Figure A1. Differences in number of patients required to satisfy 95% confidence level (blue) and the difference in the average number of patients between DH days and WPSH days (orange) for each bin of daily maximum air temperature.
Atmosphere 16 00693 g0a1
Atmosphere 16 00693 g0a2
Figure A2. Probability distribution of the number of heat stroke patients [/million/day] in the (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures for the DH (red) and WPSH (blue) days. The solid curves represent generalized extreme value distributions fitted for the DH (red) and WPSH (blue) days. The period used is July–August 2008–2019, a period not affected by the COVID-19 pandemic. The prefectural population in 2021 was assumed.
Figure A2. Probability distribution of the number of heat stroke patients [/million/day] in the (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures for the DH (red) and WPSH (blue) days. The solid curves represent generalized extreme value distributions fitted for the DH (red) and WPSH (blue) days. The period used is July–August 2008–2019, a period not affected by the COVID-19 pandemic. The prefectural population in 2021 was assumed.
Atmosphere 16 00693 g0a2
Atmosphere 16 00693 g0a3
Figure A3. Relationship between the daily maximum temperature [°C] and the number of heat stroke patients per million people for DH and WPSH days in the (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures. The solid line shows the average number of heat stroke patients (per million people) computed for each 2 °C interval starting from 22 °C. The asterisks indicate a statistically significant difference at the 95% confidence level. The prefectural population in 2021 was assumed. The period used is July–August 2008–2019, a period not affected by the COVID-19 pandemic.
Figure A3. Relationship between the daily maximum temperature [°C] and the number of heat stroke patients per million people for DH and WPSH days in the (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures. The solid line shows the average number of heat stroke patients (per million people) computed for each 2 °C interval starting from 22 °C. The asterisks indicate a statistically significant difference at the 95% confidence level. The prefectural population in 2021 was assumed. The period used is July–August 2008–2019, a period not affected by the COVID-19 pandemic.
Atmosphere 16 00693 g0a3
Atmosphere 16 00693 g0a4
Figure A4. Differences in the 2 m specific humidity [g/kg], surface downward shortwave radiation [W/m2], and 10 m wind speed [m/s] between DH and WPSH days in the (a,d,g) Tokyo, (b,e,h) Osaka, and (c,f,i) Aichi prefectures. The shading illustrates the averaged values for cases within temperature ranges that show significant differences as depicted in Figure A3. The dots represent areas at which the difference between the DH and WPSH days is significant at the 95% confidence level. The period used is July–August 2008–2019, a period not affected by the COVID-19 pandemic.
Figure A4. Differences in the 2 m specific humidity [g/kg], surface downward shortwave radiation [W/m2], and 10 m wind speed [m/s] between DH and WPSH days in the (a,d,g) Tokyo, (b,e,h) Osaka, and (c,f,i) Aichi prefectures. The shading illustrates the averaged values for cases within temperature ranges that show significant differences as depicted in Figure A3. The dots represent areas at which the difference between the DH and WPSH days is significant at the 95% confidence level. The period used is July–August 2008–2019, a period not affected by the COVID-19 pandemic.
Atmosphere 16 00693 g0a4

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Figure 1. (a) Magnitude of the water vapor flux [g/kg m/s] (shading), geopotential height [m] (contours), and wind [m/s] (vectors) at 925 hPa and (b) the geopotential height [m] (shading) and wind [m/s] (vectors) at 200 hPa averaged over July and August for the period of 1991–2020. The blue box indicates the location of the study region, which encompasses the area surrounding Japan.
Figure 1. (a) Magnitude of the water vapor flux [g/kg m/s] (shading), geopotential height [m] (contours), and wind [m/s] (vectors) at 925 hPa and (b) the geopotential height [m] (shading) and wind [m/s] (vectors) at 200 hPa averaged over July and August for the period of 1991–2020. The blue box indicates the location of the study region, which encompasses the area surrounding Japan.
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Figure 2. Monthly variations in the number of heat stroke patients accumulated in the four prefectures during the period of 2017–2021.
Figure 2. Monthly variations in the number of heat stroke patients accumulated in the four prefectures during the period of 2017–2021.
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Figure 3. (a) Surface meteorological stations in the four prefectures. (b) Domain for the area-averaging (dashed red rectangle) used to define strong extensions of SAH and WPSH.
Figure 3. (a) Surface meteorological stations in the four prefectures. (b) Domain for the area-averaging (dashed red rectangle) used to define strong extensions of SAH and WPSH.
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Figure 4. Outline of the analysis procedure.
Figure 4. Outline of the analysis procedure.
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Figure 5. Classification results for the (a) 850 hPa and (b) 200 hPa geopotential height anomalies. The shading illustrates the composite height of the classified days, and the dots denote statistically significant differences at the 95% confidence level between the composite and climatological heights. The area-averaged height anomalies and the number of days for each WP are provided in the upper right corner. The purple boxes indicate WPs assigned as strong extensions of the (a) WPSH and (b) SAH toward Japan.
Figure 5. Classification results for the (a) 850 hPa and (b) 200 hPa geopotential height anomalies. The shading illustrates the composite height of the classified days, and the dots denote statistically significant differences at the 95% confidence level between the composite and climatological heights. The area-averaged height anomalies and the number of days for each WP are provided in the upper right corner. The purple boxes indicate WPs assigned as strong extensions of the (a) WPSH and (b) SAH toward Japan.
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Figure 6. (a) Distribution of the mean heat stroke patients [/day] for each prefecture from July to August during the period of 2008–2021. (b) Climatology (1991–2020) of the daily maximum air temperature [°C] in July and August. The gray shading represents the topography [m]. Prefecture names and station names of the prefectural capitals are indicated in panels (a) and (b), respectively.
Figure 6. (a) Distribution of the mean heat stroke patients [/day] for each prefecture from July to August during the period of 2008–2021. (b) Climatology (1991–2020) of the daily maximum air temperature [°C] in July and August. The gray shading represents the topography [m]. Prefecture names and station names of the prefectural capitals are indicated in panels (a) and (b), respectively.
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Figure 7. Frequency distributions of the daily number of heat stroke patients [/million/day] in (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures. The relative frequency represents the proportion of days relative to 868 total summer days. The daily average number of heatstroke patients, the 90th percentile value, and the 95th percentile value for each prefecture are also presented. The red line represents the daily maximum air temperature averaged over each bin. The prefectural population in 2021 was assumed.
Figure 7. Frequency distributions of the daily number of heat stroke patients [/million/day] in (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures. The relative frequency represents the proportion of days relative to 868 total summer days. The daily average number of heatstroke patients, the 90th percentile value, and the 95th percentile value for each prefecture are also presented. The red line represents the daily maximum air temperature averaged over each bin. The prefectural population in 2021 was assumed.
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Atmosphere 16 00693 g008
Figure 8. Probability distribution of the daily maximum air temperature [°C] for DH (red) and WPSH (blue) days in the (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures. The solid line represents the fitting of the kernel density function.
Figure 8. Probability distribution of the daily maximum air temperature [°C] for DH (red) and WPSH (blue) days in the (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures. The solid line represents the fitting of the kernel density function.
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Figure 9. Probability distribution of the number of heat stroke patients [/million/day] in the (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures for the DH (red) and WPSH (blue) days. The solid curves represent generalized extreme value distributions fitted for the DH (red) and WPSH (blue) days. The prefectural population in 2021 was assumed.
Figure 9. Probability distribution of the number of heat stroke patients [/million/day] in the (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures for the DH (red) and WPSH (blue) days. The solid curves represent generalized extreme value distributions fitted for the DH (red) and WPSH (blue) days. The prefectural population in 2021 was assumed.
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Figure 10. Relationship between the daily maximum temperature [°C] and the number of heat stroke patients per million people for DH and WPSH days in the (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures. The solid line shows the average number of heat stroke patients (per million people) computed for each 2 °C interval starting from 22 °C. The asterisks indicate a statistically significant difference at the 95% confidence level, applying the threshold values shown in Figure A1. The prefectural population in 2021 was assumed.
Figure 10. Relationship between the daily maximum temperature [°C] and the number of heat stroke patients per million people for DH and WPSH days in the (a) Tokyo, (b) Osaka, (c) Aichi, and (d) Fukuoka prefectures. The solid line shows the average number of heat stroke patients (per million people) computed for each 2 °C interval starting from 22 °C. The asterisks indicate a statistically significant difference at the 95% confidence level, applying the threshold values shown in Figure A1. The prefectural population in 2021 was assumed.
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Figure 11. Differences in the 2 m specific humidity [g/kg], surface downward shortwave radiation [W/m2], and 10 m wind speed [m/s] between DH and WPSH days in the (a,d,g) Tokyo, (b,e,h) Osaka, and (c,f,i) Aichi prefectures. The shading illustrates the averaged values for cases within temperature ranges that show significant differences as depicted in Figure 10. The dots represent areas where the difference between the DH and WPSH days is significant at the 95% confidence level.
Figure 11. Differences in the 2 m specific humidity [g/kg], surface downward shortwave radiation [W/m2], and 10 m wind speed [m/s] between DH and WPSH days in the (a,d,g) Tokyo, (b,e,h) Osaka, and (c,f,i) Aichi prefectures. The shading illustrates the averaged values for cases within temperature ranges that show significant differences as depicted in Figure 10. The dots represent areas where the difference between the DH and WPSH days is significant at the 95% confidence level.
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Figure 12. (a) Difference in the 2 m specific humidity [g/kg] (shading) between DH and WPSH days. Anomalies of the water vapor flux [g/kg m/s] (vectors) at 925 hPa and their magnitude [g/kg m/s] (shading) on (b) DH and (c) WPSH days with respect to the climatology (from July to August in the period of 1991–2020). (d) Difference in the zonal wind [m/s] (shading) at 200 hPa between DH and WPSH days. Latitude–height cross section of the zonal wind [m/s] (shading) at 140° E for (e) DH and (f) WPSH days. The black contour represents the climatological zonal wind averaged from July to August for the period of 1991–2020. The dots and hatches indicate a significant difference at the 95% confidence level.
Figure 12. (a) Difference in the 2 m specific humidity [g/kg] (shading) between DH and WPSH days. Anomalies of the water vapor flux [g/kg m/s] (vectors) at 925 hPa and their magnitude [g/kg m/s] (shading) on (b) DH and (c) WPSH days with respect to the climatology (from July to August in the period of 1991–2020). (d) Difference in the zonal wind [m/s] (shading) at 200 hPa between DH and WPSH days. Latitude–height cross section of the zonal wind [m/s] (shading) at 140° E for (e) DH and (f) WPSH days. The black contour represents the climatological zonal wind averaged from July to August for the period of 1991–2020. The dots and hatches indicate a significant difference at the 95% confidence level.
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Atmosphere 16 00693 g013
Figure 13. (a) Difference in the surface downward shortwave radiation [W/m2] (shading) between DH and WPSH days. Latitude and height cross section of the pressure velocity [Pa/s] (shading) at 140° E for (b) DH days, (c) WPSH days, and (d) their difference. The dots represent a statistically significant difference at the 95% confidence level.
Figure 13. (a) Difference in the surface downward shortwave radiation [W/m2] (shading) between DH and WPSH days. Latitude and height cross section of the pressure velocity [Pa/s] (shading) at 140° E for (b) DH days, (c) WPSH days, and (d) their difference. The dots represent a statistically significant difference at the 95% confidence level.
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Table 1. Climatology (1991–2020) of the daily maximum and average air temperatures [°C] in major urban areas (Tokyo, Osaka, Nagoya, and Fukuoka) during July and August.
Table 1. Climatology (1991–2020) of the daily maximum and average air temperatures [°C] in major urban areas (Tokyo, Osaka, Nagoya, and Fukuoka) during July and August.
Max Temp.Ave. Temp.
Tokyo30.6 °C26.3 °C
Osaka32.8 °C28.4 °C
Nagoya32.3 °C27.6 °C
Fukuoka31.9 °C27.9 °C
Table 2. Number and percentage of DH and WPSH days relative to 868 total summer days.
Table 2. Number and percentage of DH and WPSH days relative to 868 total summer days.
Number of Days (Days)Percentage (%)
DH days18021
WPSH days18021
Table 3. The proportion of days (%) with EHS occurrences in DH and WPSH days relative to the number of DH and WPSH days, respectively.
Table 3. The proportion of days (%) with EHS occurrences in DH and WPSH days relative to the number of DH and WPSH days, respectively.
TokyoOsakaAichiFukuoka
DH days41394339
WPSH days20222019
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Morioka, T.; Tamura, K.; Sato, T. Influence of the South Asian High and Western Pacific Subtropical High Pressure Systems on the Risk of Heat Stroke in Japan. Atmosphere 2025, 16, 693. https://doi.org/10.3390/atmos16060693

AMA Style

Morioka T, Tamura K, Sato T. Influence of the South Asian High and Western Pacific Subtropical High Pressure Systems on the Risk of Heat Stroke in Japan. Atmosphere. 2025; 16(6):693. https://doi.org/10.3390/atmos16060693

Chicago/Turabian Style

Morioka, Takehiro, Kenta Tamura, and Tomonori Sato. 2025. "Influence of the South Asian High and Western Pacific Subtropical High Pressure Systems on the Risk of Heat Stroke in Japan" Atmosphere 16, no. 6: 693. https://doi.org/10.3390/atmos16060693

APA Style

Morioka, T., Tamura, K., & Sato, T. (2025). Influence of the South Asian High and Western Pacific Subtropical High Pressure Systems on the Risk of Heat Stroke in Japan. Atmosphere, 16(6), 693. https://doi.org/10.3390/atmos16060693

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