Increased hailstorms in cities through cell merger mechanism across North America and East Asia

Domain: biology
Published: 2026-03-14
Editorial outcome: minor_revision

Abstract
Hailstorms rank among the most destructive extreme weather events globally, causing substantial property damage. While limited case studies suggest that cities may exacerbate hailstorms, the underlying mechanisms remain uncertain because of the complex physical processes. Here we examine a hailstorm formation pathway associated with convective merging process using long-term observational data and high-resolution numerical simulations. This pathway helps explain the rising frequency of hailstorms across two distinct climate regimes, North America and East Asia. We find that merger hailstorms (MHs) occur approximately twice as often and tend to be more intense than non-merging normal hailstorms (NHs), which have been traditionally considered as the primary hailstorm formation mode. Favorable environmental conditions support the initiation of multiple convective cells and their subsequent merging, a tendency that may be enhanced by anthropogenic heat in large cities. Projections from a machine-learning model indicate an increase in the MH frequency and a decrease in NH frequency in North America. Together, these findings highlight an underexplored hailstorm formation pathway and suggest that climate change and human activities may play a role in shaping future hailstorm characteristics and the associated risks.

Introduction
Hailstorms inflict billions of dollars in property damage and crop losses worldwide each year, alongside human injuries and fatalities1,2. For instance, in 2015, hailstorms in China damaged approximately 670,000 houses and directly resulted in the deaths of 57 individuals3. In recent decades, hailstorms have caused insured losses exceeding $1 billion annually in the United States4. As rapid urbanization leads to more concentrated population and heightened societal vulnerability, an increasing frequency of hailstorms will result in even greater disaster-related losses and casualties5,6.

Hailstones form within convective systems when several key conditions are met7. First, a strong and persistent updraft is crucial for the growth of hail particles. Typically, the maximum updraft must reach 15–30 m/s8,9 and sometimes exceed 40–50 m/s, to lift large hailstones10 above the freezing level to altitudes between approximately −10 and −25 °C2,11. Second, the presence of hail embryos and sufficient supercooled liquid water are essential for microphysical growth processes12. These embryos – small particles, generally ice particles <5 mm – act as nuclei that collect supercooled liquid water through processes known as riming or accretion2,13. Third, an extended period of time, typically ranging from 10 to 30 minutes, is required for particles to remain within favorable environments2,14,15. This duration is critical for hail growth and depends on both convective structures and hail growth trajectories4,16. While these three conditions are necessary for hail generation, they do not guarantee the occurrence of hailstones, as the hail formation process involves complex physical interactions across multiple scales. Additionally, aerosols further influence hailstone formation through nonlinear microphysical pathways17-19. Some studies report that increased aerosol concentrations produce larger hailstones18,20, whereas others found suppression effects21,22, highlighting strong dependence of aerosol impacts on particle types, concentrations, and environmental conditions23-25.

However, it is important to note that the processes discussed primarily focus on the formation conditions within the framework of an individual convective system. The existing theories of hail formation often overlook the fact that mergers between multiple convective systems are common in nature. Furthermore, the associated physical processes are considerably more complex than those involved in isolated hailstorms26-28. This raises a key question: do merged convective systems exhibit a hail formation mechanism distinct from that of individual convective systems? Existing case studies suggest that the merger of convective segments is associated with an immediate intensification of convection, leading to a stronger merged system29-32. However, the documentation of hail in these contexts is rather limited, and its behavior therefore requires further investigation. If the merged systems indeed produce more hail development more than the sum of their parts, does this imply that the threshold for hail formation is lower than currently understood? This could suggest that hail may be more prevalent in nature than previously thought.

To address this knowledge gap, we classify hailstorms into two types based on their formation pathways. Merger Hailstorms (MHs) occur when convective cell mergers directly trigger hailstone formation, whereas Normal Hailstorms (NHs) develop within isolated convective systems (see detailed definitions in Supplementary Fig. 1 and the Methods section). Figure 1 presents the hypothetical conceptual model of MH evolution near cities, illustrating how the rapid development of the merged system heightens hailfall hazards in urban areas. We focus on two representative regions, North America and East Asia, for their distinct climate regimes, hailstorm frequency33, population density, and urbanization development34. Using long-term radar data, we identified a total of 1,124 hailstorms, constituting one of the largest samples ever analyzed. To illustrate the cell merger process and associated mechanisms, we integrate multiple data sources including radar datasets, surface hail reports, reanalysis data, numerical simulations, and hail trajectory analyses. Furthermore, we apply an advanced machine learning model, CatBoost35, to predict changing trends in MH and NH occurrences in North America, using surface hail reports and CMIP6 datasets as the basis in our projections.

Results
Higher frequency of hailstorms near cities
Both North America and East Asia are characterized by a relatively high frequency of hail hazards3,33,36. To elucidate the distinctions between two types of hailstorms and their underlying mechanisms, Fig. 2 illustrates the spatiotemporal distributions of MHs and NHs in the densely populated areas of North America and East Asia based on radar data and surface observations from 2015 to 2019.

MHs account for 63.5% of total hailstorms in North America (Fig. 2a), approximately 74% more than NHs (Fig. 2b). This finding highlights that the convective cell mergers between different systems dominate the hail formation pathway over the eastern regions in North America, revealing an aspect previously unrecognized in the literature. Hailstorms near urban areas warrant increased attention due to their potential to cause more damage compared to those over croplands37. Figure 2a shows that MHs are more likely to occur within urban regions (UBA, as indicated by the orange contours). Notably, several frequency hotspots of MHs are located near the New York Metropolitan Area (NYM). The median MH occurrence in UBA (0.42 hr/yr) is nearly 35% higher than that in rural-land areas (RUL, 0.31 hr/yr, the bottom-right bars in Fig. 2a). The difference in MH frequency between UBA and RUL is statistically highly significant (p < 0.001), indicating a robust association between urban proximity and higher MH occurrence. In contrast, NHs do not exhibit a comparable enhancement near urban areas, and the UBA-RUL differences are not statistically significant (p > 0.05, Fig. 2b). Together, these results suggest that urban environments are associated with a higher relative occurrence of MHs compared to NHs across North America.

Similar distributions of MHs and NHs are observed in East Asia (Fig. 2c–d), with more pronounced concentrations of MHs within megacities in the Yangtze River Delta (YRD) and the Pearl River Delta (PRD) regions (Fig. 2c). The median occurrence of MHs in UBA is approximately 95% higher than that in RUL (0.78 vs. 0.40 hr/yr, Fig. 2c), with this difference increasing by 130% for the top 10% of hailstorm events (2.53 vs. 1.10 hr/yr, Fig. 2c).

Figure 2e illustrates the overall distributions of these two types of hailstorms across climate regions of North America and East Asia. The median frequency of MHs is approximately 56% higher than that of NHs (0.36 vs. 0.23 hr/yr), with a highly significant difference (p < 0.001). In addition, the frequency of hailstorms is nearly 25% higher in UBA compared to RUL with a significant difference (p < 0.001). These findings suggest that cities within these two climate regimes face a higher risk of hailstorm occurrences than their surroundings, where MHs are dominant.

We further examine the temporal variations in hail areal coverage (radar reflectivity > 60 dBZ) for both types of hailstorms (Fig. 2f) and across the two regions (Fig. 2g). The average hail area for MHs during 0–2 hours is nearly 79% larger than that for NHs (3.4 vs. 1.9 km2) with statistical significance in difference (p < 0.001, Fig. 2f). Similar temporal distributions are observed for hailstorms within UBA and RUL (Fig. 2g). Analyses conducted within each climate region obtain consistent results (Supplementary Fig. 2). These phenomena indicate that MHs not only occur more frequently but also possess greater damage potential with larger hail areal coverage. Given that prevalent hail theories have largely overlooked this merger type hailstorms, our findings emphasize the significance of this hail formation mechanism.

The analyses presented in Fig. 2 reveal consistent patterns across the two distinct climate regions of North America and East Asia, suggesting that the increased frequency and hail areas of MHs near cities are not isolated phenomena limited to specific climate regimes.

Warmer environments shift the hailstorm formation pathway
Environmental conditions play a crucial role in the development of convective systems, significantly influencing the formation of hailstones38,39, including temperature, moisture, atmospheric instability, and vertical wind shear2,33,40-43. Here, we use hourly ERA5-Land (0.1° grid spacing) and ERA5 (0.25° grid spacing) to characterize the environmental conditions at the initiation time of convective cells (when maximal reflectivity exceeds 40 dBZ). Among the environmental parameters examined (Supplementary Fig. 3), temperature at 2-m height (2m-T) and Convective Available Potential Energy (CAPE) show statistically significant differences between MHs and NHs in both climate regions (p < 0.05, Fig. 3a), with higher values generally observed for MHs. These favorable thermal and unstable conditions may promote an increase in convective initiation and subsequent cell mergers for MHs, which will be elaborated in Fig. 4. In contrast, dynamic forcing, as measured by the 850–1000 hPa vertical wind shear and 500–1000 hPa vertical wind shear, is relatively weaker for MHs (Supplementary Fig. 3a). However, these differences are not statistically significant, suggesting that thermal and instability conditions are more closely associated with MH occurrence. This finding is consistent with previous studies indicating that favorable thermal condition s (i.e., higher temperatures and larger CAPE values) favor convective cell merging, leading to an increased frequency of merged systems26.

Hailstorms in UBA experience more favorable thermal and instability conditions (Fig. 3a), along with weaker dynamical forcing (Supplementary Fig. 3b), compared to those occurring in RUL across the two climate regimes. Notably, the environmental differences observed for MHs versus NHs comparisons mirror those in UBA versus RUL comparisons. This similarity suggests that urban areas tend to modify regional meteorological conditions in a way that favors the development of MHs, which may contribute to a higher frequency of hail hazards in the vicinity of cities. Our findings align with previous literature, which indicates that thermal conditions play a predominant role in enhancing convective systems under relatively weak dynamical forcing44. Associated mechanisms will be demonstrated in the next section.

To further elucidate how environmental factors shape the hailstorm formation pathway, the occurrence and relative ratio of MHs and NHs, along with their associated uncertainties, are presented as functions of temperature and CAPE in Fig. 3b–c. Generally, MHs exhibit a similar distribution pattern to NHs but occur with higher frequencies across both 2m-T and CAPE. Notably, the occurrence ratio between MHs and NHs increases steadily with rising 2m-T, exceeding four times the ratio when near-surface temperatures surpass 35 °C (green dots and line, Fig. 3b). This pattern suggests that higher environmental temperatures may favor a shift in hailstorm formation from the non-merger to merger type. A similar trend is observed for CAPE (Fig. 3c), where the ratio of MHs to NHs increases to 2.5 as CAPE exceeds 2000 J/kg. These characteristics are consistent across both regions (see Supplementary Fig. 4).

Figure 3d–e displays the distributions of hail areal coverage (radar reflectivity exceeding 60 dBZ) as functions of temperature and CAPE for MHs and NHs. In general, the hail areas of MHs significantly exceed those of NHs, increasing with rising temperature and instability, peaking at approximately 36 °C and 3800–4000 J/kg (Fig. 3d). In contrast, NH hail areas peak at around 34 °C and 2500–3000 J/kg, showing no discernible increasing variations (Fig. 3e). These results indicate that, under similar thermal and instability conditions, MHs are more likely to produce more severer hail hazards through cell-merger processes.

Overall, Fig. 3 suggests that urban environments are associated with relatively warmer near-surface conditions and higher instability, which are conducive to MH development across two climate regimes examined. Consistent with this, higher 2-m temperature and CAPE correspond to an increased likelihood of the hailstorm formation pathway shifting from the normal type to the merger type, potentially contributing to higher MH occurrence. A potential source of uncertainty is that the ERA5 0.25° dataset (i.e., CAPE) may not fully capture urban-rural heterogeneity, although many large cities in the study have horizontal extents comparable to or exceeding the 0.25° grid spacing. To address this, we perform 1,000 Monte Carlo resampling simulations using the ERA5 ensemble spread to represent reanalysis uncertainty. The resulting distributions of p values and error bars (Fig. 3a–c) indicate that accounting for this uncertainty does not change the main conclusions, supporting the robustness of our analyses.

Cities favor convective initiation and cell merger for MHs
This section documents the underlying mechanisms related to urban heat effects and convective cell mergers that promote the development of MHs in urban areas. Figure 4a illustrates the temporal variations of convective areal coverage for merger hailstorms in urban areas (MHs_UBA), normal hailstorms in urban areas (NHs_UBA), and normal hailstorms in rural-land areas (NHs_RUL). All three hailstorm subtypes show increasing convective coverage in the time window from −2.5 to 2.0 hours relative to hail occurrences, within a moving 200×200 km2 box. Particularly, the convective coverage of NHs_RUL increases by approximately 1.2% during the period from −1 to 0.5 hours, suggesting the natural development of hailstorms without the urban or merger influences. In contrast, NHs_UBA expands by nearly 1.6%, indicating an additional 0.4% increase attributable to urban impacts compared to NHs_RUL (as shown by the orange bars in Fig. 4a). Similarly, the convection of MHs_UBA grows by nearly 3.0%, suggesting a 1.4% increase due to merger impacts relative to NHs_UBA (green bars in Fig. 4a). This finding suggests that the merger process may be important for enhancing hailstorm development during the critical period from −1 to 0.5 hours prior to hail formation.

Figure 4b further illustrates the relationships among near-surface temperatures, convective initiation, and cell mergers during the development of MHs based on observational analysis and ERA5 data. As temperature rises by approximately 1.5 °C from −2.5 to 0 hours, the frequency of convective initiation increases significantly from 1 to 3 events within the study area. Consequently, the frequency of cell merger increases by more than threefold, from 0.24 to 0.83. These results indicate that higher near-surface temperatures are associated with more favorable conditions for initiating multiple convective cells, which may in turn increase the likelihood of cell mergers and subsequent MH development. Similar relationships are also observed within each individual climate region (see Supplementary Fig. 5).

What role do cities play in the processes of convective initiation and cell mergers? Human activities significantly shape regional environments and the development of convective systems44,45. While previous studies have primarily explored urban effects of aerosol emissions and land-use changes on hailstorm evolution18,46, the significant influence of anthropogenic heat (AH) on metropolitan environments has often been overlooked47-49. Unlike the radiative effects of urban land cover or urban canopy, AH represents the additional heat released into the atmosphere from buildings, manufacturing, transportation, and other human activities directly related to energy consumption48,50. This effect is often overlooked or conflated with other factors in previous studies, resulting in uncertainties in understanding urban heat effects and subsequent severe weather patterns in numerical simulations48,51,52. Here we perform statistical simulation experiments using 20 hailstorm cases (10 in North America and 10 in East Asia, Supplementary Fig. 6 and Supplementary Tables 1–2). The simulations are conducted using the Weather Research and Forecasting Model with two computational domains (at 3 km and 1 km horizontal grid spacing, respectively) and output every 5 minutes. Our findings reveal that larger AH fluxes increase near-surface temperatures in urban areas (Fig. 4c) and associated CAPE values (Supplementary Fig. 7a). These favorable conditions potentially promote the development of convective systems53-55, leading to an increase in convective initiation and subsequent cell mergers (Fig. 4c). Concurrently, the convective strength is intensified, as evidenced by stronger updrafts and more pronounced radar echoes (Supplementary Fig. 7a). Notably, the hail coverages are dramatically expanded by approximately 10%, 29%, and 71% with increasing AH fluxes (Fig. 4c). These findings are consistent across the two climate regions (Supplementary Figs. 7–8).

We examine a triple-hailfall storm event in East Asia to explore the underlying mechanisms driving merger-induced hailstorms (Fig. 4d and Supplementary Figs. 9–14). This event features both merger-type and normal-type hail formation pathways (Supplementary Fig. 13), allowing a direct comparison of these two mechanisms under similar synoptic conditions. While multiple hailfall processes within a single hailstorm have received limited attention in previous studies, we identify over 20 such cases in North America, suggesting that this phenomenon may be more common than previously recognized. During the select event, the first hailfall process arose within the isolated convective system during 1736–1754 LST prior to cell merger (observation results, Fig. 4d). As an additional convective cell began to merge with the main storm at 1806 LST (Supplementary Fig. 10b), convective updrafts intensify in the merger region (Fig. 4d and Supplementary Fig. 10f–h), followed by enhanced hail production and surface hailfall (Fig. 4d and Supplementary Fig. 11). Process-level analyses indicate that strong convergence and reduced entrainment both contribute to the rapid enhancement of updrafts, influencing both storm dynamics and microphysical processes (Supplementary Fig. 12a–b). These conditions support upward transport of liquid water, providing supercooled liquid water for rapid hail growth (Supplementary Fig. 12c). The cell merger and associated processes lead to the formation of more and larger hailstones, as confirmed by trajectory analyses (Supplementary Fig. 13). Further analyses are provided in the Supplementary Discussion.

The simulation incorporating the AH effect successfully reproduces the observed triple-hailfall processes, especially the most severe merger-type hailfall (AH4 simulation results, Fig. 4d). This simulation well reproduces the rapid enhancement of updrafts (green contours) and the corresponding increase in hailstones (white contours) following cell merger process. Notably, this convective cell involved in the merger is triggered near urban areas (Supplementary Fig. 14a–f). In contrast, simulations excluding the AH effect fail to reproduce the cell merger process and the associated multiple hailfall events (AH0 simulation results, Fig. 4d). In this scenario, updrafts remain relatively weaker due to the absence of merger dynamics (Supplementary Fig. 14g–h). These results underscore the potentially important role of the AH effect in the initiation and development of convective cells, with merger process providing a plausible pathway contributing to the higher MH frequency observed near urban regions (Fig. 2). Previous studies have shown that convective mergers significantly enhance regional updrafts and system evolution29-32. Consistent with these findings, our results suggest that the AH effect may amplify such merger-driven impacts on hail formation by promoting convective initiation and cell merger processes.

Increasing frequency of future MHs in North America
Our results suggest that warmer conditions are associated with a higher frequency of MHs, potentially through more frequent convective initiation and cell mergers. A critical question is whether this trend will persist in a warming climate in the future56. Limited understanding of hail formation mechanisms and incomplete model parameterization pose challenges and uncertainties57 for direct simulations of hailstorms. To address these issues, we utilize an advanced machine learning model, CatBoost, trained on a dataset of 3,118 surface hail reports from North America, along with a total of 17 environmental parameters, to project potential changing trends in the occurrence of MHs and NHs (see the Methods section for details). Our tests indicate a high overall prediction accuracy of nearly 97% for the three subtypes: no hailstorms, MHs, and NHs. Validation against observations for the overlapping period of 2015‒2019 shows small differences ranging from −3.0% to +2.0% (Supplementary Table 6). Furthermore, these results demonstrate strong robustness and consistency across different computing environments during testing. Using this model and CMIP6 outputs, we then project future trends in MH and NH occurrences under various Shared Socioeconomic Pathways (SSP126, SSP245, SSP370, and SSP585)58.

Generally, MH frequency is projected to increase in the future (red bars, Fig. 5a–d), whereas NH frequency decreases across all four future scenarios (blue bars, Fig. 5a–d), with these changes statistically significant at p < 0.05. The divergence between MHs and NHs is more pronounced under stronger warming scenarios (SSP126 to SSP585), and the proportion of MHs increases with rising temperatures across these scenarios (green and yellow lines, Fig. 5a–d). Multiple environmental factors contribute to the contrasting behavior of MHs and NHs, with low-level temperature and moisture emerging as particularly influential in our analysis (Supplementary Figs. 15–16). Higher values of both variables are generally associated with greater convective instability, favoring convective development and hailstorm formation.

Figure 5e illustrates the occurrences of MHs and NHs in relation to rising temperatures under various future scenarios. NHs show a clear decreasing trend, indicating that their occurrence may decrease by nearly half with a temperature increase of 2.5 °C. In contrast, the occurrence of MHs remains stable until a temperature increment of 1 °C, after which the rate of increase accelerates. Hence, the ratio of MHs to NHs continues to increase as temperatures rise (Supplementary Fig. 17). Figure 5f presents the probability distributions for the occurrence increments of MHs with rising temperatures. As the temperature increment increases from 2 °C to 4 °C, the probability of a 50% increase in MH frequency rises from nearly 18% to approximately 30–40%. Similarly, when the temperature increment changes from 4 °C to 4.5 °C, the probability of doubling MH frequency dramatically increases from 20% to 40%. These findings indicate that the occurrence of MHs is nonlinear, with a rapid increase in frequency occurring when the temperature increment exceeds 3.5 °C. The intensity of the urban heat island (UHI) contributes additional temperature increases beyond the background warming trend. With an average UHI intensity of 2.2 °C in the US59, the general climate warming of 1.3 °C is expected to reach this nonlinear threshold for a rapid increase in MH occurrence around the 2040s to 2050s for the eastern North America (Fig. 5a–d).

Our findings underscore the importance of considering both climatic trends and anthropogenic influences when examining merger-related hailstorm formation in future hailstorm projections. Given the uncertainties inherent in CMIP6 data, these projected changes in hailstorm occurrence are intended to illustrate the potential influence of higher temperatures on hail formation, rather than to provide precise quantitative predictions.

Methods
Radar data observations
Radar data provides fine-scale convective structures and evolution processes at the kilometer scale with minute-level interval, making it the optimal observational dataset for studying the dynamic processes of hailstorm formation2. The hailstorm identification and classification in this work are determined by radar observations.

The mosaic radar observations in North America were obtained from Iowa State University Mesonet62 with a 5-minute temporal interval from April to August during 2015–2019. The radar data covers the continental United States of (66°–126° W, 24°–50° N) with 0.01° gird spacing.

The mosaic radar observations in East Asia were derived from radar images with a 6-minute temporal interval over South China from March to June during 2015–2019 and over East China from April to September during 2015–201926. The two regions cover (109.5°–117.5° E, 19.0°–26.1° N) and (112.0°–123.0° E, 28.0°–35.0° N) with a resolution of 0.01°, respectively.

Hailstorm identification and verification
Radar reflectivity increases sharply with the diameter of particle (Z~D6), hence pure rain rarely produces radar reflectivity exceeding 55 dBZ theoretically2,63. Therefore, the 55 dBZ threshold has been widely adopted as a key indicator for hailstorm and performs well around the world64-68. However, detecting hail signatures aloft does not guarantee the presence of hailstones at surface due to the melting process during descent. To address this, our study implements several measures to improve the robustness of hailstorm identification, as detailed below. Additionally, this research primarily focuses on the mechanisms of hailstorm formation pathway, rather than aiming for the precise identification of surface hailstones.

First, we raised the threshold from 55 dBZ to 60 dBZ. Previous studies indicated that this critical threshold effectively increases the surface hail validation to 93–97%69,70. We included only cases with at least 4 pixels (>4 km2) showing radar reflectivity exceeding 60 dBZ for more than two consecutive scans. These two criteria were implemented programmatically to identify potential hailstorms (programs and data are provided, please see the Code Availability section).

Subsequently, all results are manually reviewed to exclude highly organized convective systems such as tropical cyclones or squall lines. This is because these systems exhibit strong dynamical structures in which the hail particles are formed through a mechanism distinct from the merger-driven formation pathway.

Second, we verified the hailstorm identification results with surface hail reports from both climate regimes. For North America, we have collected all surface hail reports from Iowa Environmental Mesonet (https://mesonet.agron.iastate.edu/cow/) within the study areas in North America during the period from 2015 to 2019. Each record is plotted alongside radar reflectivity, considering only hail located within 20 km of convective systems (>40 dBZ) within a 10-minute time window. A total of 683 hailstorms (3,118 surface hail reports) were verified out of total 835 events, resulting in a verification ratio of 81.8%. The remaining 152 cases were examined, with their maximum radar reflectivity generally exceeding that of the verified cases (Supplementary Fig. 18). For East Asia, it is important to note that the comprehensive collection and immediate verification of hailstones at the surface present significant challenges, especially in remote areas that lack human inspections. We reviewed official media platforms from weather bureaus in South China and examined all records from 2015 to 2019. Among the 106 identified hailstorms, each was accompanied by severe weather warnings. Notably, 44 cases occurred within residential areas and were verified with photos and/or video evidence (Supplementary Fig. 19). There were 58 cases out of the remaining 62 cases transpired in remote locations, including mountains, forests, or water bodies, where immediate manual recording is not feasible. Additionally, four cases occurred in remote villages, where underdeveloped Internet and social networks may have hindered documentation, or residents may have lacked awareness of the need to record such events. Therefore, while public evidence supports approximately half of the cases, the absence of documentation in the remaining cases does not imply the absence of hail at the surface.

Third, we conduct cross-verification with another independent hydrometeor identification method based on polarimetric radar variables. The classified hail particles show a high degree of consistency with the results obtained from this method (purple pixels and 60 dBZ contours in Supplementary Figs. 10–11). Notably, the 60-dBZ method identifies smaller areas of hail particles compared to the polarimetric hydrometeor classification, indicating more stringent criteria and thereby reinforcing the robustness of the hailstorm identification results. In summary, the elevated thresholds and validations described above significantly enhance the credibility of this hailstorm detection method.

After verifications across two climate regimes described above, a total of 835 cases were identified in North America while 289 cases in East Asia.

Hailstorm tracking and classification
The hailstorm tracking algorithm is implemented after identifying the hail signal from radar reflectivity, specifically, when 4 pixels exceed 60 dBZ over a duration of at least 3 frames. The system’s moving direction and speed of movement are calculated based on the spatial similarity of consecutive frames, prior to the tracking and calculation. Subsequently, the hailstorm is spatially shifted to the center of a 200×200 km2 box, spanning from −3 to 3 hours relative to the first hail signal. Note that the cell-merger analysis focuses on intense convective cells with the potential to produce hail; therefore, convective cells in this study are defined as regions with radar reflectivity exceeding 40 dBZ. All related variables are thereby calculated within this box, including the location of hailstorm, the areal coverage of convection (>40 dBZ), and the area of hail (>60 dBZ), among others. The algorithm tracks the convective cell prior to the merger (defined as the contact of 40 dBZ contours between convective systems) and continues to track the larger one if a hailstorm causes a split, although such splits are very rare during our manual inspections.

The convective initiation and cell merger algorithms are applied to the 6-hour dataset established earlier. For convective initiation, we identify the potential convective cell at time t as Area_t, and then calculate the convective area within this cell coverage at time t-1. If Area_t-1 < 0.5 and Area_t < 4 pixel, the frequency of convective initiation is recorded. For cell merger, we compare the convection of two consecutive frames. If the previous one contains more than one convective cells according to connectivity principle, the cell merger process is considered to have occur. The logic diagrams for these algorithms are included in Supplementary Fig. 20. Additionally, the program and related data are provided in the Code Availability section.

Two formation pathways for hailstorms are identified based on whether the cell merger directly leads to their formation. Specifically, when the hailstone signature (radar reflectivity >60 dBZ) appeared within a half-hour window after a cell merger, this case is classified as a merger hailstorm. Otherwise, the hailstorms are categorized as normal hailstorms. Supplementary Fig. 1 provides the conceptual models for the evolution of these two types of hailstorms, along with observational examples. All classifications were initially pre-processed programmatically and then manually verified. Sensitivity tests of the storm-tracking algorithm and key threshold choices show strong consistency, with mean trajectory differences generally within 2% and maximum differences below 5%.

Urban area identification
The urban land cover data is obtained from the MODIS dataset. Notably, the urban normally has an impact on convective systems in metropolitan areas and downwind of city centers44,71,72. Therefore, we extend the urban-influencing region to an equivalent radius around the urban land cover, with this equivalent radius calculated from the original equivalent areal coverage for each isolated urban land cover73. This urban-influencing region and urban land cover together are termed as urban areas (UBA) in this work, and the rest are termed as rural-land regions (RUL). We have tested that the changes of influencing radius do not change the key conclusions of this work.

Environmental conditions and uncertainty estimate
The environmental conditions in Fig. 3 are averaged using the ERA5-Land and ERA5 reanalysis at the time of convective initiation (when maximal reflectivity exceeds 40 dBZ) for each hailstorm event. The ERA5-Land product provides 2-m temperature at a grid spacing of 0.1°, while the remaining environmental parameters and their associated ensemble spread are obtained from ERA5 reanalysis at a grid spacing of 0.25°. For each hailstorm, the grid point closest to the hailstorm center is first identified, and the surrounding eight grid points are also included to compute the environmental parameters.

To quantify uncertainty in the ERA5-based environmental analyses, we perform 1,000 Monte Carlo simulations. In each simulation, the environmental fields are perturbed by sampling from a normal distribution with a standard deviation given by the ERA5 ensemble spread. This procedure yields a range of p values and error bars, as shown in Fig. 3a–c.

Hailstorm analyses from observations and simulations
The results in Fig. 4a are calculated within a moving box of 200×200 km2 centered on hailstorms tracked using the program (examples included in the Code Availability section). Each hailstorm tracking result is temporally aligned with the first hail signal designated as time zero. The frequencies of convective initiation and cell merger in Fig. 4b are also derived using this program. The temperatures are averaged within the study areas.

Ten hailstorms near the New York city and ten hailstorms near the Pearl River Delta Region are selected to address the impact of AH effect on the genesis of hailstones (Supplementary Table 1). As an important additional heat source in urban canopy model, the AH participates the thermal balance in the simulations via the following processes74:

ρc_p V (dT_c/dt) = (X+Q_F,c)A_c, (1)

where ρ is the air density, c_p is the specific heat of air at constant pressure, V is air volume, T_c is air temperature, t is time, Q_F,c is the AH flux released, A_c is the bottom area of characterized buildings and street in canopy model, and X is other thermal variables. Thus, the AH flux directly contributes to the increase in near-surface temperatures, distinct from the radiative interactions of urban surface land cover. The distribution of AH flux in urban areas is uneven, depending on three subtypes of residential areas: low-density, high-density, and commercial residences. Each subtype exhibits diurnal variations, with two peaks at 08 and 17 LST (Supplementary Fig. 6c), reflecting levels of human activities such as traffic75,76.

High-resolution simulations are conducted using the Weather Research and Forecasting Model (WRF4.0). Two domains are used with 3 km and 1 km grid spacings (shown in Supplementary Fig. 6a–b) at an output interval of 5 minutes. Four groups of sensitivity experiments are independently conducted for each hailstorm case, only differing in the values of AH flux from 0 to 400 W/m2 (Supplementary Fig. 6c). The Milbrandt-Yau double-moment microphysical scheme is applied12. More information on model configuration is provided in Supplementary Table 2.

All results presented in Fig. 4c are calculated within a moving box of 200×200 km2 centered on the hail locations. The temperatures and CAPEs in Supplementary Fig. 7 are calculated specifically for urban areas. The updrafts and radar echoes are determined in regions where radar reflectivity exceeds 10 dBZ, which is considered the threshold for cloud cumulus formation77. The convective initiation (CI), cell merger (CM), and hail grid in Fig. 4c are derived within the study box. The frequency of CI and CM are obtained using the program (provided in the Code Availability section), while the hail grid is counted when grid points exceed 0.1 mm of accumulated hail at the surface.

Triple-hailfall storm analyses: radar data and retrievals
We conducted an in-depth analysis of a triple-hailfall event that occurred near the Pearl River Delta Region on March 19, 2016 (Supplementary Figs. 9–14). This event was selected as it featured two distinct hail formation mechanisms including merger pathway and normal pathway, providing a unique opportunity for the comparative analysis of hail growth processes under the consistent synoptic environments. Additionally, this event took place near a megacity, where ample observations enable robust cross-validation of urban effects on hailstorm evolution.

Three S-band radar observations were collected to analyze the structure and evolution of the storm under study, including one polarimetric radar (Supplementary Fig. 9). The three radars operate in the VCP21 mode consisting nine elevations (0.5°, 1.5°, 2.4°, 3.4°, 4.3°, 6.0°, 9.9°, 14.6°, and 19.5°). Radar reflectivities and three-dimensional wind fields are retrieved and interpolated onto a Cartesian Coordinate system with 1-km horizontal and 0.5-km vertical resolution using the NCAR LROSE software (https://www.eol.ucar.edu/software/lrose-core). Hydrometeor classification (Supplementary Figs. 10–11) was conducted based on the polarimetric radar variables using an updated S-band fuzzy logic algorithm (https://github.com/CSU-Radarmet/CSU_RadarTools). All variables in the first plot of Fig. 4d and supplementary Fig. 12 are calculated within a moving 60×60 km2 box centered on this hailstorm.

Two sensitivity experiments of AH4 and AH0 are set with a peak value of anthropogenic heat flux of 400 and 0 W/m2. The results presented in the latter two plots of Fig. 4d are derived from model outputs, including radar reflectivity (dBZ, shadings), updrafts (m/s, green contours), and hail content (g/kg, white contours).

Triple-hailfall storm analyses: hail trajectory model
Hail growth trajectories in Supplementary Fig. 13 are derived from observational retrievals using dual-Doppler wind method, calculated retrospectively by positioning hailstones within the storm at various heights. The dual-Doppler method utilizes radial velocities from at least two radars to retrieve the three-dimensional wind fields through an iterative process based on the equation of mass conservation78. The hailstone sizes are estimated using polarimetric variables from observations79.

To focus on reasonably possible hail trajectories, simplified assumptions are applied in this study. After testing several hail growth models, three fundamental equations are selected for the growth of hail particles80. The diffusional growth rate of hailstones is negligible compared to the accretional growth rate; hence, the mass growth equation is:

dm/dt = A_T V_T LWC, (2)

where m (kg) is the mass of hailstones, A_T (m2) is the cross-sectional area of a particle and LWC (kg/m3) is the cloud liquid water content derived from the sounding data81; V_T (m/s) is the particle terminal velocity, determined by the radius R (m) of hail particles82:

V_T = 485.2×R^0.8. (3)

The shape of particles is considered to be spherical and no melting process is included, hence the mass-diameter equation83 is formulated as:

m = 27905×R^3.53. (4)

For melting process, the mass equation is taken84 as:

(1/(c_w T + L_f)) dm/dt = -χ 2πR N_Re N_Pr (k_a T + N_Sc L_v Δρ_v a_v), when T > 0. (5)

Corresponding descriptions of these variables are listed in Supplementary Table 3. The storm-relative wind fields are further interpolated onto the storm-center coordinate every 1 minute. We aim to trace the origin of hail particles, emphasizing the impacts of merger process on hailstone genesis before melting. Accordingly, terminal hailstones are positioned at varying heights above the melting layer (about 4 km) at 18:48 LST and tracked backward using a 10s-time step. When hail particles descend below the melting layer, the melting process is calculated forward shaping hailstones prior to the landing on the surface. The hailstone trajectory model is provided in Code Availability section.

Hailstorm projections
Future hailstorm projections are conducted utilizing an advanced machine learning model, CatBoost, based on a dataset of 3,118 surface hail reports and 17 environmental parameters across North America. By training this model on current hailstorm occurrences under present mesoscale environmental conditions, we can project the future hailstorm occurrences in relation to climate trends. The process for making future hailstorm projections consists of four primary steps.

Step One: building the training dataset. We collected 2,082 surface hail reports of MHs and 1,036 records of NHs across North America (verified aforementioned). We select 17 mesoscale environmental parameters from ERA5 reanalysis which are commonly recognized as important for severe weather development and accessible from CMIP6 dataset simultaneously. These parameters include temperature (at multiple layers), specific humidity (at multiple layers), instability (CAPE), and wind shear (at various layers), as detailed in Supplementary Table 4. For each surface hail report, we identified the spatially nearest point within a 2-hour time window from two hours prior to the hail signal using the hourly ERA5 reanalysis at the horizontal resolution of 0.25°. This temporally expanded approach increases the sample size, allowing us to train a more precise model. Consequently, the training sample size for MHs is 2,082×3=6,246 and the sample size for NHs is 1,036×3=3,108 (Supplementary Table 4). Next, we establish a time window from 24 hours to 3 hours before the hail signal to create training samples for no hailstorms. We excluded any grid points with convection (> 40 dBZ) within 0.5° longitude/latitude radius. This process yielded a total of 46,446 samples for no hailstorms (see Supplementary Table 4).

Step Two: training the CatBoost model. CatBoost is an advanced decision tree-based model that uses gradient boosting algorithm to calculate the importance score of each contributing feature during training (https://catboost.ai/). CatBoost stands out due to its advanced architectures, including symmetric trees and ordered target encoding, which help reduce prediction bias and overfitting. These features collectively enhance the model’s accuracy with each training iteration. As a result, CatBoost generally outperforms traditional random forest models85,86 in weather prediction. In this study, the CatBoost model predicts three categorical features of MHs, NHs, and no hailstorms. This model is provided in Code Availability section. The dataset is split into sample sizes of 80% for training to 20% for testing, respectively (see Supplementary Table 5). The trained model achieves a high overall prediction accuracy of 96.85% on the testing data, indicating strong robustness and consistency across various computational environments. Detailed evaluation metrics are presented in Supplementary Table 5. Interpretability of machine learning model is as important as their predictive performance. To assess the importance of each environmental parameter, we employ the permutation importance method, which quantifies the global influence of individual features by measuring the performance degradation when their values are randomly shuffled87. The results are provided in Supplementary Fig. 15.

Step Three: building the CMIP6 environmental dataset. There are notable discrepancies between ERA5 and CMIP6 in terms of underlying physical processes, output variables, and spatiotemporal resolution. Hence, it is inappropriate to directly use CMIP6 outputs to predict hailstorm occurrence with the CatBoost model, which is trained on mesoscale environments derived from ERA5. To bridge the gap between climate scale of MCIP6 and mesoscale characteristics captured in ERA5, we use the training samples of environmental parameters from Step One as a baseline, and then extrapolate the mesoscale environments based on projected climate change trends. For example, if CMIP6 indicates that the average 2-m temperature increases by 0.4 °C during 2020–2024 compared to 2015–2019, we assume that the mesoscale 2-m temperatures will also increase by 0.4 °C on average. This approach captures the climate change trends while maintaining consistency between historical and projected mesoscale environmental conditions. However, this method does not fully capture the spatiotemporal variability of climate changes. As such, this work primarily focuses on average trends in projected hailstorm occurrences.

Step Four: hailstorm projections under future scenarios. Using the trained CatBoost model from Step Two and future mesoscale environmental conditions in Step Three, we can project future hailstorm occurrence. This study examines four future scenarios (i.e., SSP126, SSP245, SSP370, and SSP585) from three climate models (BCC-CSM2, CAS-ESM2, NorESM2). Each model provides monthly average atmospheric data with an approximate spatial resolution of 100 km. The results presented in Fig. 5 are aggregated from the three models for each future scenario.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This manuscript describes how merging storms amplify hail occurrence and occur preferably over urban areas. While this is a highly relevant subject, the title overstates the findings as the analyses are performed in 2 regions, not globally; and the climate change analysis appears very preliminary. Moreover, the manuscript covers a lot of material, but does not go into sufficient detail to let the reader fully understand the data / presented processes. I do think the subject is important to publish, but especially the later part of the manuscript needs more convincing analyses and perhaps the manuscript would benefit from being split, or have a longer form to allow for more detailed discussions.", "reviewer_2": "I thank the authors for their positive responses to my review comments. I see that the manuscript has been substantially revised. It now includes data from both the USA and China and modelling studies from both regions. The focus of the paper is now more on urban effects on hailstorms, which I think is more suited to the data and conclusions from the study. However, of the issues I raised in my previous review, some major ones remain:\n1. Hailstorms are defined using only radar reflectivity. The extra corroboration the authors have added apply only to the storms observed in Asia and even only about half could be corroborated by reports. The authors should at least corroborate US reports as well, and ideally would use MESH or POH as I suggested in my last review.\n2. Melting is still not taken into account. As I outlined previously this is a crucial lack because surface hail occurrence depends on melting. The authors state that the paper concerns only hail “formation pathways”, not surface hail, but it is surface hail that matters for damage and human injury and about which most readers will be interested.\n3. The future projections remain far too simple, as I outlined in my last review. The projections are based only a linear relationship between observed temperature and hailstorm frequency and severity. As I outline below this is too simple a relationship that misses crucial details. Given the projections are not convincing, the frequent statements in the paper about increasing severity and frequency of hailstorms are not supported by solid evidence and, for frequency, are not in agreement with other work in the literature.\nWhile I acknowledge the extensive work that the authors have put into their revised manuscript I do not believe the manuscript is yet ready for publication in its current form. I hope that my comments below will help the authors to further improve the article. And, as I mentioned previously, I think that the results on difference in hailstorms between cities and urban areas are of great importance and relevance. Changes in hailstorms with global warming are also of great relevance but must be done in a more rigorous way than is shown here.", "reviewer_3": "I am grateful for the opportunity to revisit the manuscript by Zhou et al., originally submitted to [REDACTED] and now under consideration for Nature Communications. After carefully reviewing the authors’ comprehensive responses to the first-round reviews and the revised manuscript, I am confident that they have addressed all concerns thoroughly and significantly strengthened the study. The reasons are listed as follows:\nIn my initial review, I highlighted several specific points, all of which the authors have addressed satisfactorily:\n• Title and Key Terms: The inclusion of “merger-formation” in the title sharpens the study’s focus, making it more aligned with the central mechanism.\n• Spatial/Temporal Analysis: The revised Fig. 2 incorporates hailstorm lifespans, providing a more robust spatial distribution analysis. While the 5-year dataset remains short for long-term trends, the authors appropriately limit projections to mechanistic insights rather than precise forecasts, clarifying both applications and limitations (Lines 528–533).\n• Environmental Parameters: The analysis now includes potential temperature, CAPE, and vertical wind shear, with ERA5 reanalysis data averaged over a 3-hour pre-hail window (Methods, Lines 453–458). This detailed thermodynamic/dynamic characterization strengthens the mechanistic discussion, particularly the role of urban-induced warmth in enhancing storm mergers.\n• Regional Comparisons: The North America analysis is a pivotal addition, demonstrating that merger-driven hailstorms occur in diverse climatic contexts, which greatly enhances the study’s broader significance.\nBesides, the authors have demonstrated a thoughtful and rigorous approach to addressing feedback from all reviewers, particularly the constructive critiques from Reviewers #1 and #2. The revisions reflect a deep engagement with the scientific issues raised, including dataset limitations, methodological uncertainties, and the generalizability of conclusions. Therefore, the revised manuscript by Zhou et al. represents a significant advancement in understanding how urban environments and climate warming influence hailstorm severity through convective cell mergers. The authors have meticulously addressed all reviewer concerns, improving dataset robustness, methodological clarity, and mechanistic depth. The study’s focus on a understudied formation pathway—merger-driven hailstorms—coupled with cross-regional validation, makes it a valuable contribution to climate and hydrometeorology literature.\nGiven the thorough revisions and the absence of unresolved issues, I enthusiastically recommend this manuscript for publication in Nature Communications without further revisions.", "reviewer_4": "Review of “Increased Hailstorms in Cities through Cell Merger Mechanism across North America and East Asia” submitted to Nature Communications.\nThis is an interesting study about the urbanization impact on hail frequency. The authors put a lot of effort in conducting observational analysis and model simulations. It should be a very nice study for a domain science journal. There is a significant science gap to meet the standards of the high-impact journals in my view.\n1. Hail production and frequency are strongly related to aerosol environment and cloud microphysical processes. However, aerosol impact and cloud microphysics are neglected in this study. Besides the impact on thermodynamic environment, urbanization elevated aerosol concentration a lot, which can significantly enhance hail frequency, as shown from literature studies. While urbanization increases near-surface temperature, it also induces drying due to reduced latent heat fluxes, which might not strengthen storm, particularly may not favor the formation of condensates including hail in the storms. In my view, the increased aerosol concentrations from the cities can be a significant factor contributing to the increased hail frequency. But observational analysis and WRF model simulations did not consider this factor. In the WRF model simulations, the cloud microphysical scheme employed in the model is not even mentioned. This is important because some schemes do not even consider hail microphysics.\n2. Another significant problem is the future hail frequency estimates. Future hailstorm projections using the environmental conditions from CIMP6 climate models is very problematic. This has been the common knowledge in the community. The science has been advanced to the state that convection permitting model simulations are needed to study the environmental factor impacts on hailstorms. Also, future aerosol impacts are not considered. As in the future, aerosol concentrations are likely less thus could reduce hail frequency. I think such crude estimate does not help the science advance (it is going backwards). This study can be better off without this part which has too large uncertainty.\n3. Third, the environmental conditions analysis is problematic. “The environmental conditions in Fig. 3 are averaged based on the ERA5 reanalysis from 3 hours prior to the emergence of hail signature for every hailstorm event” – the method should not be appropriate. First the meteorological conditions from environment for a convective storm should be from the time before the convective initiation. Three hours prior to the emergence of hail signature could be in the middle of the convective system, which would not be representing the environment for convective initiation. Also, note that ERA5 data might not have the resolution high enough to distinguish urban and rural cases, and have large biases.\nSpecific comments:\nAbstract:\nL18-120: “Here, we introduce a novel hailstorm formation pathway, merger hailstorms (MHs), identified through long-term observational analysis and high-resolution numerical simulations” This sentence is confusing and needs clarification.\nFigure 2e. how can the bar for RUL or UBA represent both types of the storms as there is only one bar? Is it for the MH?\nFigure 2f and g, to my understanding the title of the x-axis for these two panels are wrong. There should be “time to 1st hail signal”. This applied to the title of the x-axis in other figures that have the same x-axis.\nIn the main text, at each section, at least need the brief information about the data and methods used for each major analysis. Currently the main test does not have any information about the methods, which makes the readers very difficult to gain a good understanding of the results. In high impact journals, There's no space for detailed method description however, key method information for example what data and what model and model resolution should be included."}, "author_response": "We sincerely appreciate the thorough reviews and constructive feedback from the reviewers. Specifically, they raised several concerns, primarily regarding: (1) the methodology presentation and code availability; (2) clarification of mechanisms (e.g., convective initiation and melting); (3) hail verification for the US; (4) future projections; (5) language and expressions.\nIn response, we have made substantial revisions to the manuscript, updated all the figures, and provided necessary supplementary materials along with the associated programs. Below, we highlight the key modifications to address the concerns raised by the reviewers:\n(1) Methodology and Code Presentation: We have clearly presented the methodology utilized in this study, including hailstorm identification, tracking, and classification; convective initiation; cell merger; hailstone trajectory calculation; and the machine learning procedures for training and projection. For further details, please refer to the responses below and the Method section of the revised manuscript. Additionally, the relevant programs are provided in the Code Availability section for reference.\n(2) Mechanism Clarification: First, we have analyzed the interactions between convective initiation and cell merger in Fig. 4 and Supplementary Figs. 5–8, using both observations and simulations, to reveal their relationships with rising temperatures. Second, we have provided a detailed analysis of a representative hailstorm case in Fig. 4 and Supplementary Figs. 9–14, highlighting the significance of the merger process in hailstorm development from both dynamical and microphysical perspectives. Third, we have incorporated the melting process into the hail trajectory model, as shown in Supplementary Fig. 13.\n(3) Hail Verification: We have verified 683 out of 835 cases (81.8%) using 3,118 surface hail records from the US. The remaining 152 cases were also examined, and their maximum radar reflectivity generally exceeds that of the verified cases. For additional details, please refer to the supplementary file SI_Hail_Verification_NorthAmerica.pdf.\n(4) Future Projections: We have applied an advanced machine learning method, CatBoost, based on a dataset of 3,118 surface hail reports and 17 environmental parameters across North America. The trained model achieves an overall prediction accuracy of 97%, demonstrating strong robustness and consistency across different computing environments. The model and training data are provided in the Code Availability section.\n(5) Language and Expressions: We have moderated the language throughout the revised manuscript, with a focus on presenting the results across two climate regimes.\nOur point-by-point responses are provided below in blue. We sincerely hope that these revisions adequately and satisfactorily address the comments and concerns raised by the editors and reviewers."}]

Decision letter