Extreme Wind Speeds Research Articles

Regional frequency analysis of extreme wind in Pakistan using robust estimation methods

The quantile estimates of extreme wind speed are needed for various areas of interest using regional frequency analysis (RFA) and extreme value theory. These calculations are crucial for the coding of wind speed. The data was taken from the NASA official website at a 10-meter distance and measured in meters per second (m/s). RFA of annual maximum wind speed (AMWS) using L-moments is performed utilizing annual maximum wind speed data from sixteen sites (16) in Pakistan’s Khyber Pakhtunkhwa province. There are no sites that are found to be discordant. The wards method is used to construct a homogenous region and make two homogenous regions from 16 sites. The heterogeneity test justifies that both clusters are homogeneous. The most appropriate probability distribution from the Generalized Normal (GNO), Generalized Logistic (GLO), Pearson Type-3 (P3), Generalized Pareto (GPA), and Generalized Extreme Value (GEV) distributions is chosen to calculate regional quantiles. According to the L-moments diagram and Z statistics, the GEV for Cluster- Ι and GLO for Cluster- ΙΙ are the best suggestions from the others. Both clusters’ robustness is measured utilizing relative bias (RB) and relative root mean square error (RRMSE). Overall, GEV distribution is fit for cluster-Ι, and the GLO distribution is fit for cluster-ΙΙ. Utilizing the site mean and median as index parameters, we can also find at-site quantiles from regional quantiles. The study’s quantile estimates can be employed in codified structural designs with policy consequences.

The state of the ocean in the northeastern Atlantic and adjacent seas

Abstract. In this paper, the Copernicus Ocean State Report offers detailed scientific analysis of the ocean under climate change, ocean variability, and ocean extremes in the northeastern Atlantic and adjacent seas. Major results show that the northeastern Atlantic Ocean and adjacent seas have experienced consistent warming, with sea surface temperatures increasing at a rate of 0.25 ± 0.03 °C per decade since 1982, doubling the global average trend. This warming is most pronounced in the Black Sea, Mediterranean Sea, and Baltic Sea. Sea levels have risen significantly over the past 30 years, particularly in the Baltic and Mediterranean seas. Ocean acidification has also increased, with pH decreasing at a rate of −0.017 ± 0.001 units per decade. Marine heatwaves have intensified and expanded, affecting over 60 % of the region in 2022 and 2023. Over the past 16 years, most extreme wind speeds exceeding 22 m s−1 prevailed in the central and subpolar North Atlantic and northern Mediterranean Sea. The region has also seen significant variability in ocean climate indicators and circulation patterns, including increased Atlantic Water transport to the Arctic Ocean through the Fram Strait and notable variations in the Mediterranean Sea's meridional overturning circulation. No major Baltic inflow occurred in winter 2022/23.

Revisiting design wind speed and cyclonic factor for east coast of India

It is very much challenging to estimate the accurate design wind speed for engineering structures of India, those experiencing extreme wind events such as cyclones. However, there are different categories of structures based on their importance level and a trade-off always exists between structural safety and economy. Indian code IS15498 specifies uniform cyclonic factors. However, a wide variation has been found while considering different return periods and threshold wind speeds above which cyclones have been modelled. Accordingly, a cyclonic factor (k4) has been calculated using three distinct approaches. These include the Gumbel distribution with the Generalized Least Squares Method (GLSM), the Generalized Extreme Value distribution with the Least Squares Method, and the Peaks over Threshold approach employing the De Haan method. These methods were applied to the dataset of peak values from all cyclone events to determine the design wind speed and, subsequently, the cyclonic factor. The analysis shows that Gumbel distribution with GLSM provides the best results with 1.03 value of cyclonic factor. Reverse Weibull can also be used to fit the extreme wind speed data. The outcomes from all approaches consistently underscore the necessity of a cyclonic factor for the east coast of India.

Evaluation of future wind climate over the Eastern Mediterranean Sea

The aim of this study is to evaluate wind climate characteristics in the Eastern Mediterranean Sea until the end of the 21st century using the wind fields simulated by the EURO-CORDEX Regional Climate Models (RCM), namely the Rossby Centre regional atmospheric model (version RCA4). Long-term averages, long-term trends, and long-term variability in wind characteristics under two Representative Concentration Pathway (RCP 4.5 and 8.5) scenarios were assessed for two future periods: the near future for the years 2021–2060 and the middle future for the years 2061–2100. Relative to the historical period, the most remarkable changes in the mean wind speeds were projected under the RCP 8.5 scenario in the middle future in the central and southern Aegean Sea (exceeding 5 % increase) and in the eastern and northern Levantine basin (up to 5 % decrease), compatible with the definition of the climate scenarios as the RCP8.5 scenario assumes a rising radiative forcing until 2100. The changes in extreme wind characteristics are more noteworthy in agreement with the indicators of climate change (i.e., an increase in the intensity and the frequency of extreme events). 100-year return period wind speed increases by about 29 % in the western Levantine basin under the RCP 8.5 scenario while the most drastic decrease in 100-year extreme wind speed was found in the central Levantine basin with a reduction of exceeding −29 % for the middle future period under the RCP 8.5 scenario. These decreasing and increasing behaviors in extreme wind speeds are expected in the study area as the Mediterranean is affected by many climate systems.

Evaluation of Seasonal Prediction of Extreme Wind Resource Potential over China Based on a Dynamic Prediction System SIDRI-ESS V1.0

Wind resources play a pivotal role in building sustainable energy systems, crucial for mitigating and adapting to climate change. With the increasing frequency of extreme events under global warming, effective prediction of extreme wind resource potential can improve the safety of wind farms and other infrastructure, while optimizing resource allocation and emergency response plans. In this study, we evaluate the seasonal prediction skill for summer extreme wind events over China using a 20-year hindcast dataset generated by a dynamical seamless prediction system designed by Shanghai Investigation, Design and Research Institute Co., Ltd. (Shanghai, China) (SIDRI-ESS V1.0). Firstly, the hindcast effectively simulates the spatial distribution of summer extreme wind speed thresholds, even though it tends to overestimate the thresholds in most regions. Secondly, high prediction skills, measured by temporal correlation coefficient (TCC) and normalized root mean square error (nRMSE), are observed in northeast China, central east China, southeast China, and the Tibetan Plateau (TCC is about 0.5 and the nRMSE is below 0.9 in these regions). The highest skills emerge in southeast China with a maximum TCC greater than 0.7, and effective prediction skill can extend up to a 5-month lead time. Ensemble prediction significantly enhances predictive skill and reduces uncertainty, with 24 ensemble members being sufficient to saturate TCC and 12–16 members for nRMSE in most key regions and lead times. Furthermore, we show that the prediction skill for extreme wind counts is strongly related to the prediction skill for summer mean wind speeds, particularly in southeast China. Overall, SIDRI-ESS V1.0 shows promising performance in predicting extreme winds and has great potential to provide services to the wind industry. It can effectively help to optimize wind farm operating strategies and improve power generation efficiency. However, further improvements are needed, particularly in areas where prediction skills for extreme winds are influenced by smaller-scale weather phenomena and areas with complex underlying surfaces and climate characteristics.

Uncertainty of typhoon extreme wind speeds in Hong Kong integrating the effects of climate change

Uncertainty of typhoon extreme wind speeds in Hong Kong integrating the effects of climate change

Joint probabilistic modeling of extreme wind-wave conditions under typhoon impact and applications to extreme response analysis of floating offshore wind turbines

The joint probabilistic modeling of extreme wind and wave conditions under typhoon impact is a critical task for extreme response analysis of floating offshore wind turbines (FOWTs) deployed in typhoon-prone areas. However, wind and wave observations under typhoon impact are quite limited, which poses great challenges to the probabilistic modeling task. Although some sophisticated programs can be employed to simulate typhoon-induced wave fields, they suffer from unbearable computational burdens. To improve the accuracy and efficiency of joint probabilistic modeling of extreme wind and wave conditions under typhoon impact, an approach that synthesizes the typhoon hazard analysis method and copula-based conditional modeling method is proposed in the present study. The proposed joint distribution model is expressed as the product of the distribution of extreme wind speed and the conditional distribution of significant wave height. The distribution of extreme wind speed is estimated through the typhoon hazard analysis method, and the conditional distribution of significant wave height is modeled by the copula-based method. The effectiveness of the proposed method is verified by comparing with observation data. Applications of the proposed method to extreme response analysis of FOWTs are illustrated as well.

The application of the analysis framework for compound extreme event dependencies in China

AbstractThe escalation of compound extreme events has resulted in noteworthy economic and property losses. Recognizing the intricate interconnections among these events has become imperative. To tackle this challenge, we have formulated a comprehensive framework for the systematic analysis of their dependencies. This framework consists of three steps. (1) Define extreme events using Mahalanobis distance thresholds. (2) Represent dependencies among multiple extreme events through a point process‐based method. (3) Verify dependencies with residual tail coefficients, determining the final dependency structure. Applying this framework to assess the extreme dependence of precipitation on wind speed and temperature in China, revealed four distinct dependency structures. In northern, Jianghuai, and southern China, precipitation heavily relies on wind speed, while temperatures maintain relative independence. In northeastern and northwestern China, precipitation exhibits relative independence, yet a notable dependence exists between temperatures and wind speed. In southwestern China, precipitation strongly depends on temperature, while wind speed remains relatively independent. The Qinghai–Tibet Plateau region displays a significant dependence relationship among precipitation, wind speed, and temperature, with weaker dependence between extreme wind speed and temperature. This framework is instrumental for analyzing dependencies among extreme values in compound events.

Aggregation of data from multiple recording stations for extreme wind analysis and prediction

Accurate, reliable and robust techniques for the probabilistic estimation of extreme wind speeds are essential for the design of structures for wind loading. Aggregating gust wind data from various stations with similar, homogeneous wind climates into a ‘superstation’ for hazard analysis has been employed since the 1980′s to reduce the effects of sampling errors. A concern that has been raised recently is that prediction biases may arise from such aggregation, when the data exhibit non-homogeneity due to inevitable short data lengths or imperfect homogeneity of the wind climates. By Monte Carlo simulation, we show that superstation aggregation is an unbiased technique for high recurrence level estimations when an appropriate fitting method is used, and the apparent biases are dependent on the method used for fitting the hazard model. To ensure homogeneity, we introduce a de–trending technique for minimizing any biases in the aggregated wind data. Four model-fitting methods for superstation analysis are compared, and shown that the introduced de-trending method is effective for eliminating the biases due to sampling errors and non-homogeneity.

Interpretable extreme wind speed prediction with concept bottleneck models

Concept-bottleneck models (CBMs) are a new paradigm to construct interpretable classifiers. The CBM architecture can be regarded as a neural network with a single hidden layer whose neurons are replaced by binary classifiers. Each of these binary classifiers implements a concept, that is, it attempts to answer a meaningful yes/no question: the presence or absence of a concept in the observation presented to the network. The output layer is usually implemented as a linear stage that combines the concepts into final decisions. This paper presents an application of the CBM paradigm to a problem of Extreme Wind Speed prediction, such that the forecasting properties of the system can be interpreted in terms of different concepts considered for this problem. A significant contribution of this paper is the proposal of an automated concept generation method, with controlled sparsity, in which the concepts are automatically extracted from the branches of decision trees fitted with informative features that capture the dynamics of the wind speed time series. The final forecasting system is able to predict extreme wind speed values in wind farms with good accuracy and interpretable results, as experiments over real wind speed data from a wind farm in Spain show.

A Two-Stage Operation Strategy for Energy Storage under Extreme-Heat-with-Low-Wind-Speed Scenarios of a Power System

Changes in weather conditions directly impact the output of wind power, photovoltaic systems, and other forms of uncontrollable power generation. During extreme weather events, the output from wind and photovoltaic sources is typically reduced. In light of this, this paper proposes a two-stage operational strategy for energy storage, under scenarios of extreme-heat-with-low-wind-speed, in power systems. Firstly, historical data on wind and solar power, along with weather characteristics, are collected to analyze the power output during multi-day periods of extreme heat and low wind speed. Then, Monte Carlo simulations are employed to generate multi-day load curves with inherent uncertainties, based on regional load characteristics of the power system. Finally, a two-stage operation strategy for energy storage charging and discharging is established. In the first stage, normal operations are conducted to identify periods of power shortage across various types of loads. In the second stage, based on the identified moments of power shortage from the first stage, charging and discharging constraints are applied to the energy storage systems. The feasibility and effectiveness of this two-stage operational strategy are then validated through simulations, using historical data to generate scenarios of multi-day extreme-heat-and-low-wind-speed conditions.

The influence of thunderstorm type on extreme near-surface wind speeds: Iowa case study

The derecho of August 10, 2020 that impacted Iowa and neighboring states is the costliest thunderstorm disaster in U.S. history. A derecho, which features prolonged and destructive winds, is a type of thunderstorm which has different near-surface wind generating mechanisms than typically assumed in wind engineering. The derecho event prompted two research questions with respect to wind engineering: (1) should derechos, and more broadly thunderstorm type, be considered separately? and (2) how unique is this particular derecho event?Wind speeds for design in the U.S. are currently estimated using an approach where probability distributions of all thunderstorm winds are analyzed through a mixed distribution. Using Automated Surface Observing System (ASOS) and radar data from the National Weather Service, thunderstorm events with ASOS wind speeds >58 mph (26 m/s) were classified by thunderstorm type: single-cell, multicellular, or supercell thunderstorms in Iowa. An extreme value analysis was done on each thunderstorm type. Multicellular thunderstorms, like the August 2020 derecho, dominate the extreme wind climatology in Iowa. Evaluating the uniqueness of the derecho event required a post-damage assessment. Analysis from failed and unfailed street signs and nearby anemometry was used to estimate peak wind speeds approaching 120 mph (50 m/s).

Correlation of powers of Hüsler–Reiss vectors and Brown–Resnick fields, and application to insured wind losses

Hüsler–Reiss vectors and Brown–Resnick fields are popular models in multivariate and spatial extreme-value theory, respectively, and are widely used in applications. We provide analytical formulas for the correlation between powers of the components of the bivariate Hüsler–Reiss vector, extend these to the case of the Brown–Resnick field, and thoroughly study the properties of the resulting dependence measure. The use of correlation is justified by spatial risk theory, while power transforms are insightful when taking correlation as dependence measure, and are moreover very suited damage functions for weather events such as wind extremes or floods. This makes our theoretical results worthwhile for, e.g., actuarial applications. We finally perform a case study involving insured losses from extreme wind speeds in Germany, and obtain valuable conclusions for the insurance industry.

Assessing satellite-derived wind speeds: insights from buoy measurements and analysis of deviation factors

ABSTRACT To evaluate the latest generation of satellite wind speed from AMSR2, ASCAT, GMI, SSMIS, SMAP, and WindSat, buoy measurements were obtained from buoys in coastal and tropical open ocean areas. Root mean square errors were calculated, resulting in values of 1.19 m/s for AMSR2, 1.25 m/s for ASCAT, 1.37 m/s for GMI, 1.38 m/s for SSMIS, 2.48 m/s for SMAP, and 1.18 m/s for WindSat. Numerous data pairs fall above the 45-degree line, mainly due to decreased buoy ac-curacy during high winds. Factors contributing to this include underestimation of buoy readings due to rough sea states and wind profile distortion. Satellite biases exhibit unimodal fluctuations over a one-year cycle, aligning with annual sea surface temperature variations. Statistical effects in extreme wind speed range cause abnormal fluctuations in statistics. Higher latitudes experi-ence larger deviations due to rough sea state. Ocean currents affect satellite wind speed observa-tions, particularly when aligned with wind direction. Coastal wind speed gradients primarily influenced from land contamination like rugged mountainous terrain along the West Coast of North America. Coastal upwelling also contributes to bias, with the west coast showing higher bias. A global comparison between SMAP and WindSat retrieval wind speeds demonstrates SMAP’s poor ability to monitor general wind speeds and validates the satellite-buoy experiment.

Revisiting the estimation of extreme wind speed considering directionality

In the estimation of extreme wind speed, the effect of wind direction is usually ignored. Nevertheless, as a vital parameter of the wind, it should be reasonably taken into consideration. Nowadays, the influence of wind direction can be considered through directional wind speed or directly regarding it as a variable. Based on wind speed data of climate stations, the wind directions are divided into uniform and non-uniform sectors. Copula function is then applied to establish the joint distribution of directional wind speed. The effect of the partition of directional sectors is discussed subsequently. Meanwhile, a joint distribution model of wind speed and wind direction based on copula function is also set up. According to the joint distribution, extreme wind speeds under specified wind directions are estimated based on conditional probability. Besides, extreme wind speeds considering wind directionality based on the aforementioned two methods are compared. Results show that the classification of wind direction will affect the estimation of extreme wind speed. In addition, the estimation of extreme wind speed under specified wind direction based on the joint distribution of wind speed and wind direction does not reflect the actual wind characteristics. Through the comparison of different methods considering directionality, the result based on the joint distribution of directional wind speed seems more reasonable.

Comparing annual extreme winds in Iran predicted by numerical weather forecasting and Gram-Charlier statistical model with meteorological observation data

Iran has been experiencing severe dust storms due to strong wind speeds that cause sand erosion. This erosion is particularly pronounced in the semi-arid and arid zones. Wind speed predictions using numerical weather forecasting (NWF) are indispensable; however, the applicability of NWF for predicting extreme annual wind speeds is not well known. To validate the NWF model, this study compared NWF-model-predicted 3-hour averaged wind speeds over one year and those observed at 390 weather stations. In addition, a statistical model was employed to estimate the probability densities of wind speeds for both the observational data and the NWF output. As an NWF model, the mesoscale Weather Research and Forecasting (WRF) model was utilized to simulate wind speeds. The results indicate that the observation data are consistent with the modeled datasets regarding the relationships among the mean, standard deviation, and skewness, whereas the WRF model tends to overestimate the mean wind speeds. In addition, the predictability of annual extreme wind speeds was determined using the peak factor. Moreover, skewness has emerged as an influential parameter for predicting extreme winds. Finally, the Gram-Charlier series model was utilized to estimate probability density functions of the wind speeds, demonstrating its effectiveness in capturing positively skewed distributions. The present analyses broaden the use of both NWF outputs and statistical methods to predict extreme wind speeds in Iran.

Geostationary Satellite-Based Overshooting Top Detections and Their Relationship to Severe Weather over Eastern China

Overshooting tops (OTs), prominent signatures within deep convective storms, are produced by intense updrafts and are closely linked to heavy rainfall, strong winds, and other severe weather conditions. Using an OT dataset derived from multiyear observations of precipitation radar on board the Global Precipitation Measurement core observatory as a reference, the performances of two commonly used OT detection algorithms are evaluated for the Himawari-8 and Fengyun-4A satellites. The results indicate that the infrared contour-based algorithm based on Himawari-8 is the most effective for objective OT detection in eastern China. It exhibits a probability of detection (POD) of 62.1% and a false-alarm ratio (FAR) of 36.6%, outperforming others by achieving a greater POD and a lower FAR. Furthermore, based on the severe weather records from surface meteorological stations and nearby OT detections, a strong relationship is revealed between GEO-detected OTs and the occurrence of short-term heavy rainfall (e.g., ≥20 mm h−1) and extreme wind speed (e.g., ≥17.2 m s−1) events. The OT matched percentages for these events are 61.8% and 54.0%, respectively. This suggests that GEO satellite-based OT data can serve as an important objective product for forecasters to increase their understanding of severe convective storms.

Resolution Dependence of Extreme Wind Speed Projections in the Great Lakes Region

Abstract The effect of anthropogenic climate change on extreme near-surface wind speeds is uncertain. Observed trends are weak and difficult to disentangle from internal variability, and model projections disagree on the sign and magnitude of trends. Standard coarse-resolution climate models represent the fine structures of relevant physical phenomena such as extratropical cyclones (ETCs), upper-level jet streaks, surface energy fluxes, and land surface variability less skillfully than their high-resolution counterparts. Here, we use simulations with the NCAR Community Earth System Model with both uniform (110 km) resolution and the variable-resolution configuration (VR-CESM-SONT, from 110 to 7 km) to study the effect of refined spatial resolution on projections of extreme strong and weak wind speeds in the Great Lakes region under end-of-century RCP8.5 forcing. The variable-resolution configuration projects strengthening of strong-wind events in the refined region with the opposite occurring in the uniform-resolution simulation. The two configurations provide consistent changes to synoptic-scale circulations associated with high-wind events. However, only the variable-resolution configuration projects weaker static stability, enhanced turbulent vertical mixing, and consequentially enhanced surface wind speeds because boundary layer dynamics are better captured in the refined region. Both models project increased frequency of extreme weak winds, though only VR-CESM-SONT resolves the cold-season inversions and summertime high temperatures associated with stagnant wind events. The identifiable mechanism of the changes to strong winds in VR-CESM-SONT provides confidence in its projections and demonstrates the value of enhanced spatial resolution for the study of extreme winds under climate change. Significance Statement In this study, we compare climate change projections of high and low extreme wind speeds in the Great Lakes region between a standard coarse-resolution simulation and a high-resolution simulation performed using the same climate model. The fine-resolution simulation projects strengthening high wind speeds, opposite to the coarse-resolution simulation. Both project increasing frequency of extreme weak winds, but the human-health-related impacts of stagnant winds are only captured at fine resolution. The changes in the coarse-resolution simulation are explained by changes to large-scale circulation, while the fine-resolution changes are linked to local processes the coarse model does not resolve. This helps explain the diverging projections of strong winds and gives greater credibility to the fine-resolution simulation.

Epidemiological characteristics of tuberculosis incidence and its macro-influence factors in Chinese mainland during 2014–2021

BackgroundTuberculosis (TB) remains a pressing public health issue, posing a significant threat to individuals' well-being and lives. This study delves into the TB incidence in Chinese mainland during 2014–2021, aiming to gain deeper insights into their epidemiological characteristics and explore macro-level factors to enhance control and prevention.MethodsTB incidence data in Chinese mainland from 2014 to 2021 were sourced from the National Notifiable Disease Reporting System (NNDRS). A two-stage distributed lag nonlinear model (DLNM) was constructed to evaluate the lag and non-linearity of daily average temperature (℃, Atemp), average relative humidity (%, ARH), average wind speed (m/s, AWS), sunshine duration (h, SD) and precipitation (mm, PRE) on the TB incidence. A spatial panel data model was used to assess the impact of demographic, medical and health resource, and economic factors on TB incidence.ResultsA total of 6,587,439 TB cases were reported in Chinese mainland during 2014–2021, with an average annual incidence rate of 59.17/100,000. The TB incidence decreased from 67.05/100,000 in 2014 to 46.40/100,000 in 2021, notably declining from 2018 to 2021 (APC = -8.87%, 95% CI: -11.97, -6.85%). TB incidence rates were higher among males, farmers, and individuals aged 65 years and older. Spatiotemporal analysis revealed a significant cluster in Xinjiang, Qinghai, and Xizang from March 2017 to June 2019 (RR = 3.94, P < 0.001). From 2014 to 2021, the proportion of etiologically confirmed cases increased from 31.31% to 56.98%, and the time interval from TB onset to diagnosis shortened from 26 days (IQR: 10–56 days) to 19 days (IQR: 7–44 days). Specific meteorological conditions, including low temperature (< 16.69℃), high relative humidity (> 71.73%), low sunshine duration (< 6.18 h) increased the risk of TB incidence, while extreme low wind speed (< 2.79 m/s) decreased the risk. The spatial Durbin model showed positive associations between TB incidence rates and sex ratio (β = 1.98), number of beds in medical and health institutions per 10,000 population (β = 0.90), and total health expenses (β = 0.55). There were negative associations between TB incidence rates and population (β = -1.14), population density (β = -0.19), urbanization rate (β = -0.62), number of medical and health institutions (β = -0.23), and number of health technicians per 10,000 population (β = -0.70).ConclusionsSignificant progress has been made in TB control and prevention in China, but challenges persist among some populations and areas. Varied relationships were observed between TB incidence and factors from meteorological, demographic, medical and health resource, and economic aspects. These findings underscore the importance of ongoing efforts to strengthen TB control and implement digital/intelligent surveillance for early risk detection and comprehensive interventions.Graphical

Effects of extreme meteorological factors and high air pollutant concentrations on the incidence of hand, foot and mouth disease in Jining, China.

The evidence on the effects of extreme meteorological conditions and high air pollution levels on incidence of hand, foot and mouth disease (HFMD) is limited. Moreover, results of the available studies are inconsistent. Further investigations are imperative to elucidate the specific issue. Data on the daily cases of HFMD, meteorological factors and air pollution were obtained from 2017 to 2022 in Jining City. We employed distributed lag nonlinear model (DLNM) incorporated with Poisson regression to explore the impacts of extreme meteorological conditions and air pollution on HFMD incidence. We found that there were nonlinear relationships between temperature, wind speed, PM2.5, SO2, O3 and HFMD. The cumulative risk of extreme high temperature was higher at the 95th percentile (P95th) than at the 90th percentile(P90th), and the RR values for both reached their maximum at 10-day lag (P95th RR = 1.880 (1.261-2.804), P90th RR = 1.787 (1.244-2.569)), the hazardous effect of extreme low temperatures on HFMD is faster than that of extreme high temperatures. The cumulative effect of extreme low wind speeds reached its maximum at 14-day lag (P95th RR = 1.702 (1.389-2.085), P90th RR = 1.498(1.283-1.750)). The cumulative effect of PM2.5 concentration at the P90th was largest at 14-day lag (RR = 1.637 (1.069-2.506)), and the cumulative effect at the P95th was largest at 10-day lag (RR = 1.569 (1.021-2.411)). High SO2 concentration at the P95th at 14-day lag was associated with higher risk for HFMD (RR: 1.425 (1.001-2.030)). Our findings suggest that high temperature, low wind speed, and high concentrations of PM2.5 and SO2 are associated with an increased risk of HFMD. This study not only adds insights to the understanding of the impact of extreme meteorological conditions and high levels of air pollutants on HFMD incidence but also holds practical significance for the development and enhancement of an early warning system for HFMD.