In this study, we utilize the Community Earth System Model version 1.3, employing the Community Atmosphere Model 5.0 (CAM5) as its atmospheric component. CAM5 runs at two spatial resolutions: nominal 1 and 0.25°. Sea surface temperature (SST) and sea ice are prescribed at a spatial resolution of 1.0°×1.0°. Atmospheric gas chemistry and aerosol processes are simulated using the Model for OZone And Related chemical Tracers (MOZART) and the three-mode version of the Modal Aerosol Module (MAM3). The high-resolution and low-resolution configurations of CESM are denoted as SW-HRESM and CESM-LR, respectively. Further details can be found in Gao et al. (2025b). The simulation period covers June to August from 2015 to 2019, with May used for spin-up to mitigate initial condition influences. Prior to that, a half-year spin-up is applied due to the fact that tropospheric ozone initially starts from zero. Specifically, we used a total of 7 months of spin-up, ensuring that the influence of initial conditions was fully minimized, thereby guaranteeing the accuracy of the tropospheric ozone simulation.

Emissions for the simulations are sourced as follows: global anthropogenic emissions from the Copernicus Atmosphere Monitoring Service (CAMS-GLOB-ANT v4.2-R1.1; Granier et al., 2019), with updates for China based on the Multi-resolution Emission Inventory for China (MEIC; Li et al., 2017). Volcanic emissions are from the Global Emission Inventory Activity (GEIA), and aircraft emissions are from the Community Emission Data System (CEDS). Biomass burning emissions data are sourced from the Fire INventory from the National Center for Atmospheric Research (FINN) version 2.5 (Wiedinmyer et al., 2023). High-resolution simulations use emissions data at 0.1° resolution, while low-resolution simulations aggregate emissions from 0.1° to ∼ 1.0° resolution. Biogenic emissions are calculated online using the Model of Emissions of Gases and Aerosols from Nature version 2.1 (MEGAN2.1; Guenther et al., 2012). Further emission details are available in Gao et al. (2025b).

Two numerical experiments are designed to assess the impact of BVOC emissions on ozone. The first experiment includes all emissions (base case), while the second experiment turns off BVOC emissions (no-BVOC case). By subtracting the results from the no-BVOC case from those of the base case, we isolate the contribution of BVOC emissions to ozone.

To identify atmospheric blocking, we use a two-dimensional hybrid blocking index based on 500 hPa geopotential height. The index is applied across a range of latitudes, ϕ (40 to 75° N), for each longitude, λ, incorporating meridional gradients to identify blocked grid points: GHGNλ,ϕ=Zλϕ+Δ-­Zλ,ϕΔ<­10,GHGS(λ,ϕ)=Z(λ,ϕ)-Z(λ,ϕ-Δ)Δ>0,Zanomaly(λ,ϕ)=Z(λ,ϕ)-Z‾(ϕ)>0, where GHGN (GHGS) indicates the meridional gradient to the north (south) of geopotential height at 500 hPa, Z means the 500 hPa geopotential height at longitude λ along latitude ϕ, and Z‾ is the zonal (0 to 360°) average of Z at latitude ϕ; Δ is set as 15°.

A blocking region is defined when the meridional extension of blocked grid points exceeds 15°. The center of each blocking region is determined as the grid point with maximal 500 hPa geopotential height. Sequential blocking events are identified if the center of a blocking region on 1 d was within a specified distance (27° in latitude × 36° in longitude) of the center on the previous day. We restrict a blocking event lasting at least 5 d. More information can be found in Masato et al. (2013) and Gao et al. (2025a).

To examine Rossby wave propagation, the horizontal stationary wave activity flux (W) is calculated following Takaya and Nakamura (2001). Key variables used for flux calculation include zonal wind (U), meridional wind (V), wind speed (U), and anomalous geopotential height (Φ′): 1W=Pcosϕ2U⋅Ua2cos2ϕ∂ψ′∂λ2ψ′∂2ψ′∂λ2+Va2cosϕ∂ψ′∂λ∂ψ′∂ϕψ′∂2ψ′∂λ∂ϕUa2cosϕ∂ψ′∂λ∂ψ′∂ϕψ′∂2ψ′∂λ∂ϕ+Va2∂ψ′∂ϕ2ψ′∂2ψ′∂ϕ2, where W represents the wave activity flux (unit: m² s⁻²), ψ′ (=Φ′/f) represents the geostrophic stream function, f (= 2Ωsinϕ) is the Coriolis parameter, P is the normalized pressure (P per 1000 hPa), and a is Earth's radius. λ and ϕ denote the longitude and latitude, respectively.

Observational ozone data are collected from several platforms, including the Air Quality System (AQS; https://www.epa.gov/aqs; last access: 30 June 2023) and the Clean Air Status and Trends Network (CASTNET; https://www.epa.gov/castnet; last access: 30 April 2023) in the US, the European Monitoring and Evaluation Programme (EMEP) database (http://ebas.nilu.no; last access: 30 January 2023) in Europe, and the China National Environmental Monitoring Centre (CNEMC; http://www.pm25.in; last access: 8 December 2021) in China. The monitoring network comprises 1293 sites for AQS, 99 for CASTNET, 286 for EMEP, and 2025 for CNEMC. Meteorological data used in this study are sourced from the National Centers for Environmental Prediction (NCEP) Reanalysis-1 (Kalnay et al., 1996).

Figure 1 illustrates the characteristics of observed ozone levels based on a comprehensive analysis of extensive observational datasets, with ozone exceedance rates shown in Table 1. Peak season ozone (Fig. 1a), as defined by the WHO in 2021, is determined using a 6-month running average of maximum daily 8 h ozone concentrations for each grid, with the maximum value being considered. The WHO air quality guideline is set at 60 µg m⁻³ (31 ppbv; level II), with additional standards of 100 µg m⁻³ (51 ppbv; level I) and 70 µg m⁻³ (36 ppbv; Air Quality Guideline). Regional differences in ozone pollution are apparent: higher concentrations are observed in the western US due to elevated altitude and background levels (Parrish et al., 2021a). Specific sites with significant ozone pollution include Los Angeles and Houston, as previously documented (Dunker et al., 2017). In Europe, ozone pollution is more pronounced in southern regions, particularly around the Mediterranean, consistent with earlier studies (Zohdirad et al., 2022). In China, the eastern region exhibits concentrated pollution. Mean peak season ozone levels are 45.5 ppbv in the US, 42.9 ppbv in Europe, and 53.7 ppbv in China.

The cumulative distribution function of peak season ozone concentrations is shown in Fig. 1b using gridded data. In the US and Europe, only 15 % and 8 % of the peak season ozone concentrations, respectively, exceed level I (51 ppbv) from 2015 to 2019, whereas in China, almost 60 % exceed this threshold. However, when applying the stricter standard (36 ppbv), exceedance rates are notably high: 98 %, 89 %, and 96 % in the US, Europe, and China, respectively.

Figure 1c presents the fourth-highest MDA8 ozone values annually from 2015–2019, alongside daily values for the US, Europe, and China. The WHO has established standards at 82 and 61 ppbv, with an air quality guideline of 51 ppbv. Exceedance rates (Table 1) for the strictest guideline (51 ppbv) are 98 %, 89 %, and 99 % in the US, Europe, and China, respectively. Considering all daily values, with a sample size approximately 365 times larger than the annual fourth-highest value, the rates of ozone exceedance (i.e., exceeding 51 ppbv) are observed to be 17 % in the US, 11 % in Europe, and 31 % in China. This indicates that there are significantly more days where ozone levels exceed the threshold beyond just the fourth-highest maximum daily 8 h ozone level in these regions. This suggests that air quality issues related to ozone are more persistent and widespread than what might be inferred solely from the fourth-highest MDA8 metric.

BVOC emissions during the summer months of 2015–2019 are depicted in Fig. 2a and b, with global totals of 86.0 Tg per month in SW-HRESM and 90.7 Tg per month in CESM-LR. Isoprene emissions (Fig. 2c, d) account for nearly half of these totals, amounting to 42.3 Tg per month in SW-HRESM and 45.2 Tg per month in CESM-LR. This predominance of isoprene emissions aligns with previous studies (Ma et al., 2022; Mochizuki et al., 2020). Isoprene emissions are predominantly concentrated in tropical regions, reflecting the prevalence of dense forest cover. Our study indicates values approximately 30 % higher than those (Fig. S1) reported in Weng et al. (2020) due to previously underestimated emissions in tropical regions.

To assess the utility of high-resolution simulations, we compute the standard deviation across 16 grid points in SW-HRESM, corresponding to a single low-resolution grid (Fig. S2). The average monthly isoprene emissions during 2015–2019 are 0.63, 0.51, and 0.21 kg m⁻² over the US, Europe, and China (Fig. 2c), respectively, with mean standard deviations of 0.13, 0.11, and 0.05 kg m⁻² (Fig. S2). This ratio also applies to biogenic-emission-rich areas such as the southeastern US, southern Europe, and eastern China, highlighting the importance of using finer grid spacings for accurately capturing the spatial heterogeneity in BVOC emissions.

The spatial distribution of BVOC emissions closely correlates with the distribution of broadleaf trees (Fig. S3), which have higher emission factors compared to other plant types (Table 2 in Guenther et al., 2012). Isoprene emissions are most intense in tropical regions where broadleaf evergreen and deciduous tropical trees predominate, as well as in mid- to high-latitude belts and isolated hotspots in mid-latitudes like the southeastern US, southern Europe, and eastern China.

An exception is observed in the Amazon region, where, despite dense broad evergreen tropical forest cover, the largest isoprene and BVOC emissions occur away from the main forest area. This Amazon hotspot, noted in previous studies (Opacka et al., 2021), is influenced by key meteorological factors such as 2 m air temperature and downward surface solar radiation (Fig. S4). Specifically, areas with higher temperatures and stronger solar radiation exhibit greater BVOC and isoprene emissions. The discrepancy in temperature between CESM-LR and SW-HRESM simulations reveals nuances in emission patterns, with CESM-LR showing slightly higher temperatures that lead to increased emissions. The slightly lower temperature in higher-grid-spacing simulations in regional climate models was also reported by Pugh et al. (2013). They suggested that improved representation of forests could increase latent heat flux and thereby mitigate temperature rises through a reduced sensible heat. The study compared three grid spacings: 0.1, 0.5, and 2.0°, showing that across regions such as South America, southeastern Asia, and the southeastern US, there was a small overall difference of about 2 % in BVOC emissions on a regional scale. However, this difference could reach up to 150 % in high-emission areas.

To understand the contribution of BVOC emissions to ozone concentrations across different grid resolutions, we compare two scenarios: one with biogenic emissions included and one without. Figure 3a and b illustrate the spatial distribution of ozone concentrations averaged over the summers of 2015–2019 for both SW-HRESM and CESM-LR. Both models identify significant ozone pollution areas in the Northern Hemisphere, particularly over southern Europe, the southeastern US, and eastern China. The contribution of BVOC emissions to ozone concentrations is further detailed in Fig. 3c–f.

In SW-HRESM, BVOC emissions contribute approximately 2.2 ppbv to the global mean ozone concentrations over land, representing 7 % relative to the mean value of 31.3 ppbv (Fig. 3c, e). However, the impact of BVOC emissions on ozone concentrations is modulated by factors such as anthropogenic emissions and meteorological conditions. Regions with abundant BVOC emissions and higher ozone concentrations, such as the US, Europe, and eastern China, show a substantial contribution of 15 % to 30 % from BVOC emissions to ozone levels. In contrast, the Amazon rainforest in Brazil, despite having the highest BVOC emissions, exhibits a negative contribution to ozone levels. This is attributed to the fact that in regions with low NO_x concentrations, increased VOCs initiated by OH oxidation can lead to the formation of stable organic nitrogen compounds, through increasing organic peroxy radicals and elevating the reaction with NO₂ (Tonnesen and Jeffries, 1994). It reduces the availability of NO₂ and the subsequent photolysis such as a reduction in O3P, thereby reducing ozone concentrations (Kang et al., 2003; Unger, 2014). While this effect is evident in CESM-LR, lower-resolution simulations may overlook finer-scale variability, affecting the accuracy of quantifying the impact of BVOC emissions on ozone.

Heat waves not only accelerate photochemical reactions but also intensify BVOC emissions, thereby amplifying ozone production and exacerbating ozone pollution. Building on previous studies (Gao et al., 2012; Sillmann et al., 2013), heat waves are defined within each grid as periods when the daily mean near-surface air temperature exceeds the 90th percentile of the climatological mean, focusing on the summer period from 2015–2019 in this study. To quantify the impact of heat waves on ozone concentrations, Fig. 4 illustrates the probability distribution function (PDF) of MDA8 ozone concentrations for both the base case and a scenario without BVOC emissions, aggregated across entire summer periods and specifically during heat wave days. Given the superior capability of high-resolution simulations in reproducing heat waves and ozone concentrations (Gao et al., 2023, 2025b), we present results solely from SW-HRESM hereafter.

Several notable observations emerge. Firstly, a comparison of heat wave periods to non-heat-wave periods (solid red vs. solid blue lines in Fig. 4) reveals a noticeable rightward shift in the PDF, indicating an increase in ozone levels due to heat wave impacts, a well-established phenomenon (e.g., Gao et al., 2020; Zhang et al., 2018). Specifically, compared to non-heat-wave periods, mean ozone concentrations increase by 9.1, 9.7, and 8.4 ppbv during heat waves over the US, Europe, and eastern China, respectively. This effect is more pronounced in specific regions, such as North China (NC) with an increase of 17.6 ppbv, followed by the southeastern US (12.0 ppbv) and central eastern China (CECN) (9.0 ppbv), accounting for 12 % to 21 % of regional mean ozone levels. A previous study noted that median ozone concentrations during US heat waves from 1990–2016 could increase by 10 % to 80 % (Meehl et al., 2018).

Comparing scenarios with and without BVOC emissions (solid vs. dashed lines in Fig. 4), BVOC emissions significantly contribute to ozone enhancement during both non-heat-wave and heat wave periods. For instance, during heat waves, BVOC emissions contribute 20.9, 10.4, 14.4, and 20.5 ppbv over the southeastern US, Europe, North China, and central eastern China, respectively. A study by Churkina et al. (2017) found that biogenic emissions contributed 17 %–20 % to ozone formation in Berlin, Germany, in July 2006, with this contribution potentially increasing to 60 % during heat waves.

It is important to note that the influence of BVOC emissions persists outside of heat wave periods, particularly when downward surface solar radiation remains sufficiently high (Fig. S5). The differences in BVOC contributions to ozone between heat wave and non-heat-wave periods represent the incremental effect of BVOCs during heat waves, accounting for 7.7, 3.9, 2.0, and 6.7 ppbv over these four regions, respectively. This incremental effect constitutes 64 %, 40 %, 11 %, and 74 % of the total heat wave effects, indicating varying degrees of BVOC influence across different regions. The relatively smaller incremental BVOC effect during heat waves over North China is partly attributed to higher anthropogenic emissions and lower BVOC emissions compared to the other regions. With potential reductions in anthropogenic emissions in China, BVOC emissions could assume a more pivotal role, especially given projections of an increased frequency of heat waves in a warming climate (Gao et al., 2022, 2023).

Eastern China has emerged as a significant region grappling with severe ozone pollution. Numerous studies have endeavored to explore the driving factors, particularly in the last decade, leveraging the widespread availability of ozone data across China. For example, through the examination of ozone pollution events in North China during 2014–2017, Gong and Liao (2019) investigated ozone pollution episodes in North China from 2014–2017 and identified that under weather conditions characterized by high near-surface air temperatures, low relative humidity, and anomalous southerly winds in the lower troposphere, ozone concentrations tend to accumulate in this region. Mousavinezhad et al. (2021) utilized a multiple linear regression model to disentangle the contributions of meteorology and emissions to ozone levels in North China during 2015–2019. Their findings indicated that meteorological factors such as increased downward surface solar radiation and near-surface air temperatures accounted for 32 % of the observed ozone increase, while changes in emission precursors contributed 68 %. To elucidate the interannual variability of ozone in North China, Gong et al. (2020) employed tagged O₃ simulations with the Goddard Earth Observing System chemical transport model (GEOS-Chem) and suggested that one-third of the rise in ozone pollution days observed from 2014–2018, particularly in 2018, could be attributed to emissions transport from central eastern China. Considering the intertwined roles of meteorology and emissions, the focus shifted to examining ozone anomalies relative to their respective monthly averages, thereby minimizing the influence of emissions on ozone variability.

The study focuses on two specific regions – North China and central eastern China – to analyze days where regional mean MDA8 ozone levels exceeded 10 ppbv of their respective monthly means, defined as regional ozone pollution events. Observational data indicate a total of 131 and 89 such events in North China and central eastern China, respectively, during the summers of 2015–2019. Ozone pollution events are observed to extend meridionally (Figs. 5, 6), moving northward into northeastern China from North China (Fig. 5a, b) and covering large areas of northern and southern China from central eastern China (Fig. 6a, b).

During regional ozone pollution events, concurrent meteorological conditions typically feature higher downward surface solar radiation, 2 m air temperatures, reduced water vapor, and decreased total cloud cover, all of which favored ozone accumulation. Meteorological anomalies for each day are computed relative to their respective months, with the study testing four different methods for deriving climatology, including averages from the same day, same month, summer periods from 2015–2019, and summers from 1990–2019. They all yield comparable results. Analyzing atmospheric blocking, we find that 43 % (56 events) of regional ozone pollution events in North China and 48 % (43 events) in central eastern China are accompanied by blocking. Notably, among the 36 events where ozone pollution concurrently affected both North China and central eastern China, nearly 40 % are associated with blocking events.

The impact of blocking events on downstream meteorological conditions and ozone pollution is examined, primarily based on Rossby wave propagation, which profoundly affects large-scale circulations. For example, Ding and Li (2017) analyzed reanalysis data from 1951–2015 and found that Rossby waves originating from northwestern Europe entered the North Africa–Asia westerly jet in the upper troposphere, propagating eastward along the subtropical westerly jet. This circulation favored persistent heavy rainfall events in southern China (20–30° N). Liu et al. (2022) studied data from 1979–2020 and observed positive anomalies in summer shortwave cloud radiative effects over northern Russia, promoting the generation of Ural blocking. This blocking dynamically triggered a positive Eurasian pattern characterized by a +-+ wave train, resulting in positive precipitation anomalies in North China and strong heat waves in southern China. In addition to northwestern Europe and northern Russia, blocking also occurs over northeastern Russia. This, combined with the land–sea temperature contrast between warm northeastern Eurasia and the colder Oyashio region in the North Pacific, may induce a north- to south-tilting anticyclone, leading to increased temperatures across a wide area of China (Amano et al., 2023).

Blocking events are categorized into Euro-Atlantic, northern Russia, and North Pacific regions (Fig. 7), based on their geographical locations. Analysis of NCEP reanalysis data during the summers of 2015–2019 identified a total of 227 blocking days in the Northern Hemisphere, with approximately 50 % occurrence. Of these, 60 d occurred over the Euro-Atlantic sector, 68 d over northern Russia, and 162 d over the North Pacific. The higher frequency of blocking in the North Pacific is partly due to conducive conditions in northeastern Russia and Alaska. Notably, the sum of blocking events across these regions exceeds the total for the Northern Hemisphere, owing to concurrent events in multiple areas. High blocking frequency has previously been reported (Lupo, 2021), indicating climatologically in the Northern Hemisphere there are 30–35 blocking events per year with a mean duration of 9 d. This occurrence rate is higher than in our study, partly due to the larger frequency in winter and fall compared to summer.

Anomalies of 500 hPa geopotential height from reanalysis data and MDA8 ozone from observations during composite blocking events over Euro-Atlantic, northern Russia, and North Pacific are depicted in Fig. 7. These illustrations highlight the characteristics of Rossby wave propagation and the corresponding variations in ozone. For instance, when blocking occurs over the Euro-Atlantic (top of Fig. 7), it coincides with anomalously high pressure, triggering a wave number of 5 and resulting in high pressure over northern China. This configuration leads to high ozone anomalies over northeastern China, with scattered spots of high ozone anomalies over parts of North China and central eastern China. When blocking shifts eastward to northern Russia (middle row in Fig. 7), a positive Eurasian pattern emerges with a +-+ wave train. This pattern manifests in negative anomalies in the northern flank of China and positive pressure anomalies in central to southern eastern China, South Korea, and southern Japan. During blocking over the North Pacific, spanning northeastern Russia and Alaska (Fig. 7e), broad positive anomalies are observed in southern China. However, notable anomalies of 500 hPa geopotential height are absent in southern China, and positive high pressure is not always present during ozone pollution events (Yang et al., 2024).

To further elucidate the pathway of Rossby wave propagation, we focus on a typical blocking event from 27 June to 4 July 2019. During this period, a blocking high is situated over northern Russia and the eastern flank of the Ural Mountains (Fig. 8a). Coincidentally, another blocking event (29 June–4 July 2019) occurs over the North Pacific near Alaska. Regions with convergence of wave activity flux indicate weakened westerlies, suggesting an incoming wave train and accumulation of wave activity in these areas. This accumulation could further amplify the blocking high (Nakamura et al., 1997; Schneidereit et al., 2012), serving as a source region for Rossby wave propagation.

A strong high-pressure system over northern Russia (Fig. 8a), propagating southeastward (arrows in Fig. 8d), can be seen. This propagation stimulates positive height anomalies over central eastern China, evident in both the upper (200 hPa; Fig. 8b) and the middle troposphere (500 hPa; Fig. 8d), with a weaker signal observed at the lower troposphere (850 hPa; Fig. 8c), indicating a barotropic structure (Barriopedro et al., 2006; Sui et al., 2022). The blocking events over northern Russia may originate from the North Atlantic, as indicated by Liu et al. (2022). This is suggested by the presence of a positive geopotential height anomaly over the northern North Atlantic, which then propagates northeastward towards northern Europe and Russia. This pattern resembles the Rossby wave train with a zonal wavenumber of 5, as described in Xu et al. (2019). It originates west of the British Isles and propagates towards Lake Baikal, simulating a high-pressure system on the southern flank of China. The blocking over Alaska serves as another source of Rossby waves, propagating eastward towards the Atlantic and triggering another pathway through the Mediterranean Sea along the subtropical jet. This process further enhances high-pressure anomalies over central eastern China (Fig. 8d).

Modulated by this large-scale circulation, there is an increase in downward surface solar radiation, 2 m air temperature, reduced water vapor, and total cloud cover over areas spanning 25 to 40° N (Fig. 8g–j). These conditions contribute to widespread ozone increases in this region, extending slightly into North China and southern China (Fig. 8e, f). Comparably, when atmospheric blocking occurs over the Euro-Atlantic region, a Rossby wave propagates southeastward from the northern Atlantic. This triggers high-pressure anomalies in North China and central eastern China, creating meteorological conditions that favor anomalously high ozone concentrations (20–24 July 2017, Fig. S6). Additionally, a concurrent blocking event over the North Pacific initiates another Rossby wave propagation, which converges with the Rossby wave originating from the Euro-Atlantic blocking. This convergence reinforces the eastward propagation of the Rossby wave.

Next, we explore how atmospheric blocking influences ozone through the effect of BVOC emissions. In a previous study, significant improvements in summer blocking simulations were achieved by increasing horizontal resolution in an Earth system model with coupled atmosphere and ocean components (Gao et al., 2025a). Driven by the prescribed SST, high-resolution simulations have shown enhanced blocking frequencies, particularly over the Ural Mountains and North Pacific (Fig. S7). Therefore, the analysis below is based solely on SW-HRESM.

We composite blocking events occurring over the Euro-Atlantic sector (100 d), northern Russia (47 d), and the North Pacific (119 d), and the spatial distribution of ozone concentrations is shown in Fig. 9. The probability distribution function of ozone concentrations is shown in Fig. 10. Several distinctive features emerge. During non-blocking periods (Fig. S8a; Fig. 10), the mean ozone concentrations over North China are slightly higher (66.3 ppbv) than in central eastern China (63.3 ppbv). Among all three blocking categories, ozone concentrations over central eastern China tend to increase to a larger extent compared to North China, resulting in comparable or higher ozone concentration in central eastern China relative to North China (Fig. 9d, e, f). Specifically, blocking triggers ozone increases of 10.7, 7.1, and 5.9 ppbv when blocking occurs in the Euro-Atlantic, northern Russia, and North Pacific sectors, respectively, compared to values of 4.9, 4.2, and 2.1 ppbv in North China (Fig. 10). When blocking occurs in northern Russia and the North Pacific, the effect can extend further south from central to southeastern China. Accompanied by the blocking, an increase in downward surface solar radiation and 2 m air temperature, along with reduced water vapor and total cloud cover, emerges primarily over North China and central eastern China (Fig. S9). Despite slight differences, this feature is consistent with the observed patterns (Fig. 7b, d, f).

BVOC emissions play important roles in modulating ozone concentrations. When the blocking occurs, the effects of BVOC emissions on ozone concentrations range from 10.6 to 15.5 ppbv over North China and central eastern China (Figs. 9g, h, i, 10), with the largest effect when blocking occurs over the Euro-Atlantic sector. Consistent with the previous discussion on heat waves (Sect. 3.3), BVOC emissions play a role even in the absence of blocking (Fig. S8b), with effects of 10.8 ppbv over North China and 13.3 ppbv over central eastern China. The effect of BVOC emission on ozone during blocking is larger than during non-blocking for most cases, except over central eastern China during blocking in northern Russia, which is visible when blocking is compared to a lower temperature range (i.e., < 26°; Fig. S10). Overall, the incremental effect of BVOC emissions on ozone during blocking, similar to that defined in Sect. 3.3, is calculated, and it could reach as much as 65 % of the ozone increase during blocking in North China and 31 % of the ozone increase during blocking in central eastern China (Fig. 9j, k, l vs. Figs. 9g, h, i, 10).