Increasing extreme winds challenge offshore wind energy resilience

Domain: earth_science
Published: 2025-11-04
Editorial outcome: major_revision

Abstract
Climate change is amplifying the intensity of extreme strong winds, threatening the development and resilience of offshore wind energy systems. The ability of wind turbines to endure such conditions is determined by the fifty-year return period wind speed (U 50), a key parameter for turbine design. However, the long-term trends and spatial variability of U 50 across global oceans remain largely unexplored. Here, we utilize hourly ERA5 wind speed data at 100 meters above sea level from 1940 to 2023 to reveal a significant global increase in oceanic U 50 of 0.016 ms⁻¹ yr-1 (p < 0.01), with upward trends evident in 62.85% of coastal regions. Notably, over 40% of both commissioned and planned offshore wind farms in Asia and Europe have encountered wind speeds exceeding the design threshold of Class III turbines (37.5 ms⁻¹). More than half of these wind farms are situated in regions with increasing U 50 trends, a pattern strongly associated with changes in tropical and extratropical cyclone activity under global warming. These findings underscore the critical need to adapt offshore wind energy infrastructure to withstand evolving wind extremes.

Introduction
Offshore wind energy is a cornerstone of the global transition to renewable energy, with cumulative capacity expected to rise from 63 GW in 2022 to nearly 494 GW by 2030 (refs. 1,2). However, offshore wind farms operate in some of the most challenging environments, facing strong winds, waves, and ocean currents 3. Many high-potential offshore wind regions, such as China, Japan, and the United States, are located in cyclone-prone areas, raising concerns about the resilience of offshore wind infrastructure 4.

Recent studies reveal that extreme strong wind events over global oceans are intensifying under climate change. Strong tropical cyclones (TCs), particularly those with lifetime-maximum wind speeds above the 70th percentile of all recorded tropical cyclones, have shown a significant upward trend in maximum wind speed 5. In addition, extratropical cyclones (ETCs) are shifting poleward in both hemispheres 6, with transient eddy kinetic energy showing a pronounced increase along the winter storm tracks in the Southern Hemisphere 7 and a weakening along boreal mid-latitude summer storm tracks 8. Under a warming climate, high-resolution global climate models project both an increase in the frequency of strong TCs (maximum wind speeds exceeding 50 ms-1) and an approximate 3% increase in daily maximum wind speeds of ETCs per degree of warming in high-latitude regions 9–11. These changes are driven by enhanced latent heat flux 12, stronger upper-level winds 13 and shifts in sea surface temperature (SST) patterns 14. This strengthening wind environment threatens the safe operation of wind turbines throughout their multi-decadal lifespans, potentially leading to damage, early retirements, and economic losses 15. For instance, Typhoon Yagi (2024), the strongest autumn typhoon on record, caused the collapse of six wind turbines in Hainan, China and resulted in over US$ 8 million in damages (Supplementary Fig. 1 and Movie 1) 16, highlighting the vulnerability of offshore wind farms to extreme winds.

The parameter used to characterize the maximum wind speed a turbine can endure during its operational lifespan is called the reference wind speed (U ref), which is required to exceed the environmental fifty-year return period wind speed (U 50) at turbine hub height 17. The International Electrotechnical Commission (IEC) categorizes turbines into Classes I, II, and III, with reference wind speeds of 50, 42.5, and 37.5 ms⁻¹, respectively 18. Therefore, U 50 is a key parameter for assessing extreme wind conditions that are significant for wind turbine design. Normally, U 50 is derived from a Gumbel distribution, requiring long-term observational data for accurate estimation. However, sparse in-situ measurements 19 and the low temporal resolution and inconsistencies of satellite-derived wind products 20,21 limit the robustness of U 50 assessment. Consequently, long-term and spatially homogeneous reanalysis datasets, such as ECMWF’s ERA5 that provides historical wind speed data dating back to 1940, have been extensively used for U 50 estimation 17,22. However, the pattern and magnitude of U 50 changes remain poorly understood under the evolving and predominantly intensifying extreme wind environment, particularly in regions where offshore wind farms are operating or under development. Assessing whether existing and future turbines can endure changing wind extremes is essential for effective offshore wind energy planning and operations.

Here, we present a comprehensive analysis of the long-term trends and spatial variability of U 50 across global oceans from 1940 to 2023. U 50 is estimated by fitting a Gumbel distribution of annual maximum wind speeds over multi-decadal window (>20 years) to ensure robustness 17, with trends analyzed by shifting the time window year by year (see Methods for details). Considering the systemic underestimation of extreme wind speeds in reanalysis datasets caused by grid smoothing 23, we apply a spectral correction method to recover the missing variability in the high-frequency spectrum of wind speed. Since SST is a key indicator of global warming, we examine its relationship with U 50 changes to assess the influence of climate change. We exclude land areas from our analysis because U 50 values over land are significantly lower than the IEC design thresholds, with a spatial average of just 22.57 ms⁻¹ (Supplementary Fig. 2). Our primary aim is to assess risks to offshore wind farms by comparing U 50 values with IEC standards, focusing on both commissioned wind farms and development zones, especially in developing countries with abundant offshore wind potential but limited financial and technical capacity to harness this resource 24. By providing a detailed understanding of the evolving patterns of extreme wind over space and time, we aim to help developers and policymakers reduce investment risks and mitigate potential losses in regions experiencing significant increases in U 50.

Results
Extreme wind speed and trends over the ocean
U 50 values in oceanic regions range from below 10 ms-1 to over 50 ms-1, with higher values predominantly occurring along tropical cyclone-prone tracks 25 and in high-latitude regions, especially the Northeastern Pacific, North Atlantic, and Southern Westerlies (Fig. 1a). Our analysis shows that 36.7% of global oceanic grid cells have U 50 values exceeding the U ref of a Class III wind turbine (37.5 ms-1), while approximately 20.2% of grid cells exceed 42.5 ms-1, the U ref for Class II wind turbines (Fig. 1a). Only 0.8% of grid cells surpass 50 ms-1, the U ref associated with Class I wind turbines. For consistency, all U 50 values reported in this study are based on the complete 84-year time series, except for the analysis of U 50 trends derived from the moving time window.

To analyze the spatial distribution of U 50, we classify ocean basins into tropical (5–30°N/S) and extratropical (30–60°N/S) regions, corresponding to the dominant meteorological regimes of TCs and ETCs, respectively. The southern extratropical Atlantic, Pacific and India Ocean are grouped into a unified Southern Westerlies region because they are well-connected and unobstructed by land, resulting in a total of 11 sub-basins (Fig. 1a). The U 50 values in extratropical regions are substantially higher than those in tropical regions. A pronounced right-skewed distribution is observed in the Extratropical North Atlantic (ENA) and Southern Westerlies (SW) regions, indicating a higher frequency of extreme wind events (Fig. 1b). In tropical regions, U 50 values are higher in the Pacific Ocean, with a broader distribution in the Tropical Northwest Pacific (TNWP) and a concentrated right-skewed distribution in the Tropical Northeast Pacific (TNEP). This spatial pattern aligns with the expansive cyclone-prone zone in the TNWP and the frequent occurrence of intense cyclones in the TNEP (Supplementary Fig. 3). These patterns are consistent with those observed in altimeter data 26 and reanalysis dataset from NCEP’s Climate Forecast System Reanalysis 27.

A general upward trend in U 50 is observed across most ocean regions. Globally, the average oceanic U 50 has increased significantly at a rate of 0.016 ms-1 yr-1 over the 30-year window adopted in this research (p < 0.01, Fig. 1c). Trend analysis using continuous 20-year and 50-year windows yields similar rates of change (0.014 ms-1 yr-1 and 0.013 ms-1 yr-1, respectively), suggesting that the choice of time window does not substantially affect the trend estimate (Supplementary Fig. 4). Spatially, 68.0% of oceanic grid cells exhibit a significant increasing trend, while only 14.01% show a decreasing trend (Fig. 1d). Regionally, the tropical regions, including the Northeast Pacific (TNEP), North Atlantic (TNA), North Indian Ocean (TNI), and South Pacific (TSP), exhibit a relatively high proportion of grid cells with substantial increases in U 50. In these regions, 42.77%, 33.03%, 26.35%, and 45.34% of grid cells exhibit an upward trend exceeding 0.05 ms-1 yr-1 (Fig. 1d).

Although this study primarily examines extreme wind speeds, we also assess trends in mean wind speed to identify regions that exhibit both enhanced wind energy potential and heightened extreme wind risks. The ERA5 dataset reveals a modest but widespread increase in mean wind speeds across most oceanic regions from 1940 to 2023, except for equatorial zones and the northern Indian Ocean (Supplementary Fig. 5a). Importantly, increases in mean wind speeds frequently coincide with intensifying extreme winds, particularly in the eastern tropical Pacific, southeastern Pacific, and Southern Westerlies.

In addition, ERA5 provides hourly 3-second gust wind speed, which enables us to determine the origins of extreme wind—whether from sudden high-wind events or sustained strong winds. A significant widening of the difference is observed between gust winds and hourly mean winds (here we use the 90th percentile of the difference as an example), suggesting more frequent sudden high-wind events (Supplementary Fig. 5b). However, spatial discrepancies are observed in the north-central Pacific and northwest Atlantic when comparing ERA5-derived mean wind speed trends with those from altimeter observations 28 and the NCEI blended sea winds product 29. These differences may stem from temporal variations in data coverage, particularly in satellite-based products introduced after 1985.

Extreme wind speed and trend over the coastline
Given that offshore wind farms are primarily sited in coastal regions and that the farthest distance of commissioned wind farms is approximately 100 km from land 30, we specifically extract grid cells within 100 km of the coastline for analysis. In these coastal regions, around 16.49%, 11.25%, and 0.62% of grid cells exceed the reference wind speed thresholds for Class III, Class II, and Class I wind turbines, respectively. The coasts of Canada, England, Japan, and southern Argentina have the highest proportion of U 50 values exceeding the reference wind speed thresholds of wind turbine (Fig. 2a). Significant increases in extreme wind speeds, defined as trends larger than 0.05 ms-1 yr-1, are concentrated in tropical and subtropical coastal zones, including the Gulf of Mexico, the Caribbean, the southern and eastern United States, India, Southeast Asia, and northern Australia (Fig. 2b). Coastal regions experiencing increasing mean wind speed and concurrently strengthening extreme winds exhibit greater potential for offshore wind energy development, but also face heightened risks, such as the coasts of California, northern and eastern Europe, the Taiwan Strait, Africa, South America, and Australia (Fig. 2b, c). Conversely, concurrent declines in both mean and extreme wind speeds are detected in the Mediterranean Sea, East China, and coastal India, likely associated with the weakening and poleward shift of boreal mid-latitude storm tracks 8.

The risk assessment for fully commissioned offshore wind farms
Changes in extreme oceanic winds pose increasing risks to offshore wind farms. To assess these risks, we categorize wind farms into three risk categories based on the U 50 values of the grid cells in which they are located. These categories correspond to IEC reference wind speed thresholds: Type 1 (U 50 exceeding 37.5 ms⁻¹), Type 2 (U 50 exceeding 42.5 ms⁻¹), and Type 3 (U 50 exceeding 50 ms⁻¹), as winds exceeding these thresholds endanger turbines in each respective category. Fully commissioned offshore wind farms are predominantly located in Europe and Asia, with 136 and 157 wind farms, respectively, and with total installed capacities of 28.86 GW and 25.24 GW 31. Our analysis focuses on U 50 values and their trends at these wind farm locations. Notably, given the large spatial coverage of 0.25° × 0.25° grid cells, multiple wind farms may be situated within the same or neighboring grid cells.

In Europe, U 50 values across commissioned wind farms are closely clustered around the mean of 40 ms-1. The majority of these wind farms (74.26%) are located in regions exhibiting increasing U 50 trends, particularly in key areas such as southeastern UK and the coastal waters of Germany, Denmark, Netherlands, Belgium, and Sweden (Fig. 3a). Specifically, 48 wind farms (total capacity of 9.68 GW) are exposed to Type 1 risk, while 61 wind farms (total capacity of 18.14 GW) face Type 2 risk. Among these high-risk farms, 75.23% are located in regions where U 50 is trending upward. A temporal analysis shows a sharp increase in U 50 around 1975, followed by a temporary decline during 2005–2015, and a renewed upward trend in the past decade (Supplementary Fig. 6a).

In contrast, commissioned wind farms in Asia exhibit greater spatial variability in U 50, with values ranging from below 15 ms-1 to above 40 ms-1 (Fig. 3b). Over half of these sites show decreasing U 50 trends, with spatial variance nearly ten times greater than that observed in Europe. Fewer wind farms are currently at risk in Asia, with eight sites falling under Type 1 risk and two wind farms near the Taiwan Strait exposed to both Type 2 and Type 3 risks (total capacity of 1.45 GW). Among these risk-exposed Asian wind farms, U 50 generally shows an upward trend, though it was briefly interrupted around 1985. However, two wind farms located in South China deviate from this pattern, with trends remaining persistently flat or negative (Supplementary Fig. 6b).

The risk assessment for offshore wind farm development zones
The U 50 values and their trends exhibit broadly similar patterns across offshore wind farm development zones. In Europe, U 50 trends are predominantly characterized by significant increases (68.03% of grid cells), mainly concentrated in the North Sea, the Baltic Sea, and along the west coast of England. Conversely, declining U 50 values are primarily observed along the coastlines of Spain and Italy (Fig. 4a). Among the planned wind farms, 208 are exposed to Type 1 risk, while 292 face Type 2 risk, corresponding to a combined maximum planned capacity of 403.00 GW. Of these, 76.80% are located in areas with rising U 50 trends. The temporal evolution of U 50 across these wind farms mirrors the pattern observed in commissioned wind farms, characterized by a general upward trend interrupted by a temporary decline during 2005–2015 (Supplementary Fig. 7a).

In Asia, 61.23% of planned wind farms are located in regions with increasing U 50 trends, particularly concentrated in South Korea, Vietnam, the eastern and southeastern coasts of China (Fig. 4b). Although the mean U 50 value in planned wind farms across Asia is approximately 4 ms⁻¹ lower than that in Europe, several projects in Asia are exposed to higher risk categories. Specifically, 72 wind farms (total maximum planned capacity of 56.89 GW) face Type 1 risk, 46 wind farms (total maximum planned capacity of 42.11 GW) face Type 2 risk, and 69 wind farms (total maximum planned capacity of 40.52 GW) face Type 3 risk (Fig. 4b). Over 67% of these projects are located in regions where U 50 is increasing. The temporal trends follow a similar pattern to those in Europe, showing a sustained decline prior to the 1990s followed by a rebound after the 1990s (Supplementary Fig. 7b).

There are a total of 172 planned wind farms in North America, South America, and Australia. They are primarily located along the eastern and western coasts of the United States, the northeastern and southeastern coasts of Brazil, and the southwestern and southeastern coasts of Australia (see Supplementary Fig. 8) 31. The increasing trends of U 50 are most pronounced in the northeastern United States and in southeastern and southwestern Australia, while most other sites show decreasing trends. Compared to Europe and Asia, these regions display smaller magnitudes of U 50 trends. In the United States, more than half of the planned wind farms, with a total maximum planned capacity of 50.31 GW, are exposed to Type 1 and Type 2 risks. A substantial increase in U 50 began to emerge around 2005 and has continued to date. The remaining sites fall within a relatively low-risk range for extreme wind (Supplementary Fig. 9).

The U 50 details in developing countries
Given the critical role of developing countries in the global energy transition and in meeting the United Nations’ Sustainable Development Goal 7 to ensure access to clean and affordable energy for all, we analyze extreme wind conditions in nine emerging offshore wind markets, including Brazil, Colombia, India, the Philippines, Romania, South Africa, Sri Lanka, Turkey, and Vietnam 24. These countries have set ambitious targets for offshore wind development, which will require substantial financial commitments. For example, Colombia is planning 12 projects with a total capacity of 5 GW, scheduled for commissioning between 2032 and 2034 (refs. 32,33). India has issued tenders for offshore wind zones targeting 37 GW by 2030, with the transmission system for 10 GW estimated to cost around 0.28 lakh crore (approximately US$ 3.36 billion) 31. Vietnam aims for 6 GW by 2030, with foundation construction alone requiring investments of US$ 40–100 million 24. Brazil has advanced regulatory decrees to facilitate the legal use of offshore areas for wind projects 32. These developments underscore the need for accurate assessments of extreme wind risk to support efficient capital deployment in these countries.

Six of these nine countries, excluding Brazil, Turkey, and Romania, have over half of their coastal grid cells showing increasing U 50 trends. In Turkey, U 50 trends exhibit clear regional variations, with a significant downward trend observed near the Mediterranean Sea and upward trends near the Black Sea (Fig. 2b). In Sri Lanka, over 30% of grid cells show a trend greater than 0.05 ms-1 yr-1 (Fig. 5a). Colombia experiences different oceanic environments due to its exposure to both the Caribbean Sea and the southeastern Pacific Ocean, with extreme winds primarily concentrated along the Caribbean coast (Fig. 2b).

When compared to the reference wind speeds in IEC standards, five countries––South Africa, Vietnam, Turkey, Colombia and the Philippines––will face extreme risks (Fig. 5b). Notably, approximately 3% of coastal grid cells in both Turkey and the Philippines exceed U 50 values of 50 ms⁻¹ (Type 3 risk), with extreme wind conditions in Turkey mainly occurring in the Black Sea. Such high-wind environments pose additional challenges for floating offshore wind energy development, including increased maintenance difficulties and higher anchor costs 34.

Attribution of U 50 trends
To explore potential drivers of the observed changes in U 50, we use the relationship between SST and annual maximum wind speed (AMW), which serves as a proxy for U 50 at annual timescales. While SST is not a direct indicator of wind dynamics, it reflects low-frequency variability in the climate system and may influence storm intensity through thermodynamic and dynamic processes 35. Our analysis reveals a strong correlation (r = 0.80, p < 0.05) between spatially averaged SST and AMW at the global scale from 1940 to 2023 (Fig. 6a). Over this period, AMW increased by 1.05 ms-1 per degree of SST change, indicating a potential response of extreme winds to global warming (Fig. 6b).

The observed correlation between SST and AMW can be explained by the intensification of storm systems under a warming climate. This is because TCs and ETCs account for over 80% of extreme wind events 11,36. Our seasonal analysis indicates that most extreme wind events occur in winter months, suggesting a dominant contribution from ETCs (Supplementary Fig. 10) 37. Regions where extreme winds peak in summer or autumn align with known TC tracks, such as the North Indian Ocean, the tropical Pacific, and the tropical North Atlantic.

Observed changes in cyclone characteristics provide evidence of intensifying wind extremes. The spatial-temporal patterns of cyclone activities derived from best-track data (details refer to Supplementary Methods) are consistent with U 50 changes. Although TC frequency peaked in the 1990s and 2000s, the 2010–2023 period recorded the highest lifetime-maximum wind speeds in most basins (Supplementary Fig. 11a, b). Similarly, accumulated cyclone energy (ACE), defined as ACE = N 0 τ 0 V2 max (where is the maximum wind speed of cyclone, τ is cyclone lifetime, and N is the total number of cyclones) 38, also peaked during this period (Supplementary Fig. 11c). The spatial extent of regions experiencing Category 4 and 5 cyclones (V max larger than 58 ms-1) has expanded by 4.3% compared to the 1980–2001 baseline, particularly in the North Atlantic, consistent with the sharp increase in U 50 observed in the Gulf Stream region (Supplementary Fig. 3). ETCs in both hemispheres are shifting poleward, with notable intensification over the northeastern Atlantic in the Northern Hemisphere and near 60°S in the Southern Hemisphere 11. Storm activities have weakened in midlatitude regions, consistent with the decreasing U 50 trends around the Mediterranean Sea and China’s Bohai Sea.

Several physical mechanisms may contribute to these changes, including enhanced latent heat flux from rising SST 6,12 and increased positive anomalies of lower-tropospheric potential vorticity 11. For instance, TC intensification rates are reported to be three times higher for TCs generated under marine heatwave (MHW) environments compared to those under non-MHW conditions 12. Additionally, a warming climate intensifies upper-level winds and the meridional temperature gradient 39, further promoting the poleward shift of ETCs 6. While future U 50 projections remain uncertain and require comprehensive Earth System Model evaluations, the observed intensification of U 50 is likely to continue in tandem with warming trends 40.

Discussion and implications
This study presents a comprehensive analysis of global extreme oceanic wind speeds (U 50), their long-term trends, and their potential influences from global warming. Using gridded hub-height wind speed data from ERA5 (1940–2023) corrected for underestimation biases via spectral correction, we find that U 50 has significantly increased across 62.85% of global coastal regions over the past nine decades, with a global average trend of 0.016 ms-1 yr-1. Our results show that over 40% of commissioned and planned wind farms in Asia and Europe are exposed to U 50 levels exceeding IEC design thresholds, and more than 60% are located in regions experiencing rising U 50 trends. The changes in U 50 are closely linked to the evolving patterns of TCs and ETCs associated with global warming. Considering the continuous warming trend, regions with upward U 50 trends require more attention to mitigate damages caused by intensifying extreme wind events.

This study has several limitations. The spectral correction (SC) method applied to AMW increases U 50 by an average of 5.56 ms⁻¹ (Supplementary Fig. 12b). However, the SC method tends to underestimate U 50 in cyclone-prone regions, where original U 50 values are retained and warrants further refinement. While the current assessment reveals many high-risk areas overall, wind risks in these regions remain conservative. For offshore wind farms located in cyclone-prone regions, a more detailed downscaling of extreme wind load assessments is recommended to improve design safety. Due to the limited availability of sufficient long-term observations, direct validation of current U 50 results was not feasible; however, the SC method used here is consistent with that employed by Larsén et al. 23, which demonstrated strong agreement with in-situ measurements of AMW 23. This approach has been successfully applied in prior U 50 assessments 22,41,42. Previous studies employing empirical formulas to correct reanalysis wind speed underestimation are limited by a relatively coarse temporal resolution of 1 hour and a lack of in-situ observations exceeding 25 ms⁻¹ (ref. 29). In contrast, the SC method effectively refines U 50 to a 10-minute resolution, aligning with the IEC standard definition.

This study evaluates U 50 at a hub height of 100 m. However, in practice, turbine heights vary and have steadily increased over the past two decades, averaging 104.03 m in 2020 (ref. 43), with the tallest turbine reaching 185 m in 2024. Using power-law extrapolations, we estimate U 50 at hub heights of 80 m, 120 m, 150 m, and 200 m (Supplementary Fig. 13). Results show that U 50 and associated risk increase consistently with height. For instance, the proportion of grid cells facing Type 1 risk rises from 32.48% at 80 m to 54.28% at 200 m, while Type 3 risk coverage expands tenfold when hub height increases from 100 m to 200 m. Although taller turbines improve energy yield and economic returns 44, they also heighten vulnerability to extreme wind events.

The U 50 distribution and trends revealed in this study offer a reference for offshore wind design. However, turbine standards include more than just fifty-year return period wind speed. Other critical design criteria include extreme wind shear, extreme direction change, and extreme turbulence (IEC 61400-1). There are ongoing debates surrounding IEC standards, such as the load factors in IEC 61400-3 are based on north European climates and may not suit cyclone-prone regions 45,46. In addition, observed wind speed variability in coastal Denmark already exceeds the prescribed extreme turbulence model in the IEC 61400-1 Edition 3 standard 47, highlighting the need to localize design criteria based on regional wind behavior.

Extreme winds, especially from TCs and ETCs, are the leading cause of wind turbine failure, accounting for 54.9% of all reported failures 48. These failures can result in substantial economic losses, with damages ranging from US$ 0.64 million to US$ 6.4 million, depending on turbine type, equivalent to 10% of total investment 48. We recommend using higher design-load-class wind turbines in regions with intensified extreme winds. Additionally, resilience-informed planning is crucial to mitigate losses, limit failure propagation, and accelerate recovery, as the failure of one wind turbine can trigger a chain reaction of failures 49. In scenarios where damage from extreme winds is unavoidable, a “strong column-weak beam” design philosophy is suggested to minimize potential losses, with blades designed to fail first to protect more expensive components such as towers and nacelles 50.

In summary, this study highlights a widespread increase in extreme wind speeds across global oceans, with direct implications for offshore wind farm design, siting, and resilience planning. By quantifying U 50 trends and identifying their spatial patterns, our findings offer actionable guidance for industry stakeholders, especially in vulnerable regions. As tropical and extratropical cyclones continue to evolve under global warming, proactive adaptation, including improved risk modeling, revised design standards, and robust turbine engineering, will be essential to safeguard offshore wind infrastructure and support its long-term expansion.

Methods
Long-term, gridded hub-height wind speed dataset
The ERA5 dataset is the fifth iteration of the atmospheric reanalysis dataset developed by the European Centre for Medium-Range Weather Forecasts (ECMWF), spanning the period from 1940 onward 51. With a high spatial resolution of 0.25° × 0.25°, ERA5 provides hourly estimates of wind speed components alongside various meteorological variables. The assimilation process for ERA5 incorporates an extensive array of in-situ and remote-sensing observations. Specifically for wind speed, ERA5 integrates satellite retrievals from radiance data, in-situ measurements from radar detection, drifting buoys, ship stations, and aircraft observations 52. Distinguished from its predecessors, ERA5 is produced using the latest version of the ECMWF Integrated Forecast System (IFS), featuring enhanced model parameterizations and data assimilation methodologies. These improvements have been validated, demonstrating advancements in capturing wind patterns across global oceans 52,53.

The reliability of ERA5 wind speed data has been established through evaluations, particularly in offshore locations 54. It has widespread application in offshore wind resource estimation and extreme wind speed assessments. Examples include global offshore wind energy resource estimations by Soares et al. 55, assessments of extreme wind speeds in the U.S. 56, and on a global scale 17. In this study, we extract hourly u- and v-components of wind speed data from ERA5, covering the period from 1940 to 2023 and at 100 m above the ground, near the wind turbine hub height. ERA5 is a dataset suitable for resolving U 50 assessments because it provides data close to hub height with higher temporal resolution and sufficiently long records. To explore the differences in U 50 at various hub heights, we apply the power law to extrapolate wind speed to different hub heights as an example (details refer to Supplementary Methods). In this research, we only consider and evaluate wind speed from 85°N to 85°S since ERA5 has been reported to have a negative bias in the u- and v-components at the poles 52. For the coastal regions, a grid cell with a land area of less than 10% is defined as oceanic.

Additionally, ERA5 provides 3-second maximum-averaged wind speed for each hour, which is derived by the IFS from hourly averaged surface stress, surface friction, wind shear, and stability 57. Analyzing gust wind allows us to understand whether extreme wind originates from sustained strong winds or abrupt, intense gusts. Since ERA5 offers gust wind data at 10 m height, it is adjusted to 100 m using the power law (details refer to Supplementary Methods).

Sea surface temperature (SST)
SST is incorporated in this study to examine the relationship between changes in extreme wind and global warming. To maintain consistency, SST data were also obtained from ERA5, which assimilates the HadISST2 dataset before 2007 and OSTIA data thereafter, maintaining spatial and temporal resolutions consistent with wind speed data 57. ERA5 SST data show strong agreement with other datasets regarding the warming rate since the mid-1970s, with minor discrepancies observed between HadISST2 and the more recent HadSST4 over the preceding 10 years. The SST analysis is based on a global spatial average, weighted by latitude (details refer to Supplementary Methods).

Dataset of wind farm locations
Wind farm locations are integral to our risk assessment approach, wherein the U 50 assessment is harmonized with offshore wind farm geographical data to provide a tailored risk evaluation for offshore wind farm development. We utilize information from 4C Offshore Ltd (https://map.4coffshore.com/offshorewind/) 31, a renowned market research and consultancy firm specializing in the offshore wind industry, known for its extensive insights into up to 1800 offshore wind projects. The dataset obtained from “4C Offshore” reports a total of 821, 799, and 184 wind farms in Asia, Europe, and other regions, respectively, encompassing both commissioned and under-development projects. This dataset includes key parameters such as latitude, longitude, area, and turbine model types. Additionally, for each wind farm, the dataset specifies the maximum planned installed capacity, while the final installed capacity may be lower. Supplementary Table 1 highlights information on representative wind farms, detailing location, area, installed capacity, wind turbine types, capital expenditure (CAPEX), and investment per MW in U.S. dollars. The wind turbines in Supplementary Table 1 typically have 2–8 MW capacity, with reference wind speeds varying between 42.5 ms-1 and 70 ms-1, depending on the local extreme wind conditions and investment costs. These wind turbines are manufactured by Siemens, Vestas, Goldwind, and Mingyang Electric, among others. CAPEX ranges from around US$ 10 million to over US$ 1 billion, with an investment per MW of US$ 0.12–0.4 million, aligning with surveys in Angel (2017) 58.

Methodology for annual maximum wind speed correction
Reanalysis products such as ERA5 underestimate strong winds due to spatial and temporal averaging effects in the numerical model. These effects lead to reduced spectral energy at high frequency ranges compared to observations, for example, an 11% underestimation of annual maximum wind speed in a numerical model with a grid resolution of 25 km compared to anemometer measurements 23. Larsén et al. 23 developed a spectral correction (SC) method to correct the underestimation by filling the missing variability in the high-frequency range. The observed winds follow a slope of f−5/3 for frequencies f ~ 1 day−1, whereas model-simulated winds exhibit a steeper decay of f−3. The spectral behavior f−5/3 at high frequency is also supported by many theoretical arguments 59,60. This discrepancy arises from energy removal by the model’s dissipation mechanisms, including filtering and damping schemes. Larsén et al. 23 derived this correction using 10-minute observational wind data from Denmark, with the heights ranging between 10 m and 60 m. The correction fc = 0.8 day-1 addresses the underestimated spectral energy occurring at high frequencies (f > 0.8 day-1), while the upper bound fe = 72 day-1 corresponds to the Nyquist frequency of 10-min temporal resolution. Within this frequency range, the wind spectrum S(f) of reanalysis wind is corrected to follow the observed wind spectrum scaling of S(f) = f−5/3. Frequencies outside this range (f < fc or f > fe) remain unaltered. This approach effectively downscales 1-hour ERA5 wind speed data to a 10-minute equivalent resolution, conforming to the IEC standard, which is the recommended temporal resolution for U 50 assessment.

By modeling extreme wind exceedances as a Poisson process, the annual maximum wind speed U max can be derived from the zero- and second-order spectral moments (m 0 and m 2) of the corrected spectrum:
U max = U 0 + sqrt(m 0 / 2π) * sqrt(2ln(sqrt(m 2 / m 0) * T))
where U 0 represents the mean wind speed and T is the reference time period (here T = 1 year). The i-th order spectral moment is defined as:
m i = 2 * integral(f i * S(f) df) from 0 to infinity
where f represents frequency (Hz) and S(f) denotes the wind speed spectrum. For a comprehensive derivation, refer to Larsén et al. 23.

The SC method has been used in different reanalysis products to assess extreme winds globally 22,41,42. However, it systematically underestimates certain regions, especially cyclone-prone regions, as shown by U 50 comparisons between SC-corrected and raw ERA5 data (Supplementary Fig. 13b). This underestimation may stem from stronger cyclone signals in the high-frequency range, where the spectral slope is steeper than f−5/3, which deserves further investigation. While Larsén et al. 22 proposed a modified spectral correction for tropical cyclones (SC-TC), our evaluation found persistent underestimation even after its application. Furthermore, the SC-TC method relies primarily on empirical approximations derived from the Holland model by Ott (2005) 61, rather than direct validation against observational data in tropical cyclone regions. Given these limitations, our study employs the standard SC method for annual maximum wind speed derivation. For cases where the corrected U 50 remains underestimated, the original ERA5 values are retained as conservative estimates.

Methodology for U 50 assessment
Wind speed in regions abundant with wind resources has been shown to follow a two-parameter Weibull distribution with an exponential tail in the strong wind component, as established by previous studies 62,63. Consequently, the cumulative distribution function (CDF) of extreme wind inferred from the Weibull distribution exhibits a double exponential structure with two parameters, α and β, collectively referred to as the Gumbel distribution 64. The formula for the CDF is as follows:
P(U max; α, β) = e^(-e^(-(U max - β) / α))
where U max is the annual maximum wind speed derived from the SC method. The fifty-year return period wind speed (U 50) is the value of U max where the CDF of the Gumbel distribution equals 0.98. The rearrangement of the CDF formula allows us to derive U T at the T-year return period based on the following equation:
U T = -α * ln(-ln(1 - 1/T)) + β

Pryor and Barthelmie (2021) 17 employed four methods to estimate the parameters of the Gumbel distribution, including the maximum likelihood estimation (MLE), method of moments (MoM), parameters extrapolated from the Weibull distribution estimation (Gumbel-Weibull), and a graphical method. Among these, MLE is considered one of the most robust non-parametric iterative approaches 65,66, determining parameters by maximizing the likelihood function under the assumed Gumbel distribution 67. MoM, while less computationally demanding, is less robust as α and β can be derived from algebraic functions without iteration. The graphical method involves a linear fitting between the ordered observations and the corresponding probability densities, offering a simplified method for distribution inspection 68 and useful for testing whether the sample of annual maxima is drawn from a single Gumbel distribution 69. These approaches focus on the annual maximum wind speed, requiring data periods longer than 20 years for stable and convincing estimates 70.

The Gumbel-Weibull method first derives the parameters of the Weibull distribution using all available hourly data and then calculates α and β based on a linking function 17. This method is suitable when long-term wind speed data are lacking, as the Weibull distribution parameters can remain relatively stable even with short periods (<10 years). However, results from the Gumbel-Weibull method may show discrepancies, especially in tropical regions 17. In light of these considerations, the MLE method is the preferred choice due to its robustness and reliability, even though it involves higher computational demand.

Trend analysis of extreme wind speed
In the context of escalating extreme wind conditions in coastal and oceanic regions, exploring the changes in U 50 is imperative, as this directly impacts the seamless operation of existing offshore wind farms and the strategic planning of new installations. Leveraging the comprehensive ERA5 dataset, which spans 84 years (1940–2023), we have a valuable opportunity to derive robust estimations of U 50 under various climate conditions. For estimating U 50, previous studies recommend datasets of at least 20 years to ensure sufficient sampling of extreme events. While a 50-year period would theoretically align perfectly with the return period definition, this duration may mask temporal variability and provide fewer samples for trend detection. Finally, U 50 is calculated using overlapping 30-year windows shifted annually. To elaborate, the initial U 50 value is based on data from 1940 to 1969, and the last U 50 value spans from 1994 to 2023, resulting in a total of 55 derived U 50 values. The trend analysis of U 50 is conducted through linear regression applied to these derived values. To validate that the significant changes in U 50 are not contingent on the specific duration of the dataset, we also conduct trend analyses for U 50 derived from continuous 20, 30, and 50-year datasets (Supplementary Fig. 4).

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This paper conducted a detailed analysis of the spatial variability of 50-year-return-period (U_50) high extreme winds over the Earth's oceans to attempt to document their likely risk to offshore wind energy infrastructure. While a global atlas of U_50 is crucial and the results and the attempt are novel, the manuscript is confused about the scope of the work. The problem statement in the Introduction section is quite jumbled. Still, to the best of my understanding, this was the ultimate problem statement that the authors make: \"While reanalysis datasets, such as ECMWF’s ERA5, provide valuable historical wind speed records dating back to 1940, previous studies have remained limited to assessing U50 without considering whether significant changes have occurred in the context of climate change\". This makes the reader think that the paper will analyze U_50 from the reanalysis record (which it does) and connect it to recent climate change (which it doesn't, even after claiming it to be the shortcoming of earlier studies). To my understanding, the work quantifies the historical risk from extreme high winds using one dataset for one height and then uses that result (without providing any justification or uncertainty estimate) to project the historical risk into the future. This is a serious leap of faith. The paper in the Conclusion section touches on the fact that extreme high winds are not the only factor in the decision- making for wind farms while highlighting other variables that might be considered. Of these factors, the one thing that could have been easily included was the central value (mean/median) of the WSPD distribution. If both mean/median and U_50 increase, it would point to a certain regime of risk versus the places where U_50 increases but mean/median is stationary. Above all, the \"suggestions\" that the authors provide at the end of the study are pieced together from other studies. However, there isn't a reasonable laying down of the actionable insights gained from THIS STUDY. While I think the core of the study is reasonably impactful and deserves to be made public, the authors need to reframe the study in a way that justifies their assumptions (e.g., past trends continuing into the future), that they are very specific in their use of terminology (e.g., extreme winds - high or low?) and are cognizant about the limitations of their results (e.g., only one hub-height, only one dataset, the uncertainties of that dataset, etc.). This will require a significant amount of re-thinking, re- framing and rewriting, and possibly including a figure of a global map showing the spatial variability of high wind speed risks. I think that such an amount of work likely won't be possible during a manuscript review. So, I would advise the authors to resubmit after significant revisions. I have attached more detailed comments in the text document.", "reviewer_2": "The manuscript \"Increasing extreme winds challenge offshore wind energy resilience\" by Y. Zhao, Y. Tao, Y. Chen, and Z. Zeng addresses an important problem of shifting climatological norms under anthropogenic climate change. This study has its focus on the 50 years return wind speed, U50, at 100 m above the ground. The design of this study is justified by the needs of the win energy sector. The data analysis procedures are justified by the recommendations of the International Electrotechnical commission. The authors of the study have reported scientifically sounded, methodologically robust, and generally noteworthy results. Assessing the study from the bird-fly altitude, the study looks significant, reliable, and perhaps potentially useful. There are however certain difficulties in the study that need resolutions or at least discussion. The major drawback of the study is that it completely ignore any related physical issues. (a) The study does not differentiate between winds generated in extra-tropical and in tropical circulation systems. Extreme winds related to frontal system of extra-tropical cyclones have different generation mechanism, different effects, constrains, and therefore different statistics as compared to winds generated by convection in tropical storms. Moreover, the selected large regions include both tropical and extra-tropical areas which belong to different circulation systems with different directions of changes in extreme winds. To my opinion, such a physically careless selection of geographical regions for the results' aggregation makes this aggregation less robust for practical applications. (b) The study does not include any discussion of wind gusts. Indeed, there is a discussion of the spectral wind damping in ERA-5 data, but the proposed (in ref 37) correction has not been applied. I see this lacune as a significant gap in the study. Arguably, strong wind gusts could be more damaging for the infrastructure than strong but steady winds. (c) When assessed against previous studies of the same authors, the results of this study look somewhat confusing. Z. Zeng and co-authors have previously reported a reversal of global wind stilling. They found that before 2010 winds over lands were getting more calm. The supplement of this paper (Fig. S2) reports that extreme winds have been increasing all the time since 1970s. What is the cause of such an inconsistency? Tricks of statistical analysis? Data pre-selection? In any case I would like to see the global maps of the mean wind speed, U50, as well as their trends to be sure that the datasets still reproduce the earlier results of the same authors. Pay attention that Fig 3 gives U50 and U50 trends but there is something with color scales. There is no red color on the colorbars for the panels (a) and (b). (d) I have problems with assessing results in Fig. 4. To my view, the color scale clearly shows that large majority of wind parks experience reduction (negative) U50 trends. Nevertheless, the authors report the positive trends, i.e., increased extremes in all regions. Is this related to statistical tricks? The way how the wind park sites weighted and selected? (e) Finally, the part related to country scale analysis demonstrated the weakest relation to the physics and dynamics of the atmosphere. E.g., Turkey has two coastlines - one in the Mediterranean and another in the Black Seas. They are characterized by different wind climates. Mixing them together gives non-observable statistical mean. I would recommend to exclude this so-called country-scale aggregation of U50 from the paper entirely. There are a few less significant problems with the study that worth to mention: (1) The considered distance from the shore is 100 km. However the depth of the ocean is not taken into account. In the shallow North Sea, for example, the wind farms are placed at much larger than 100 km distances offshore, whereas along the dee Norwegian coastal waters, there are no prospects for offshore wind farms. (2) Although ERA-5 reanalysis is the state-of-the-art product, its quality varies depending on the amount of assimilated data. In the areas, like the North Sea, covered with data for a long period, the trends could be robust, but over other regions, there have improved only in XXI century with better satellite data coverage. The implications of data coverage for trend analysis must be discussed in the paper. Despite of the noted drawbacks and lack of physics in the analysis, I consider this study as strong enough to recommend its publication after the required revision. The analysis supports the conclusions, but special reservations are needed when the country-scale conclusions are presented. There are no significant flaws as such but more careful selection of statistically and dynamically homogeneous regions is needed int he revisited version. Statistical and dynamical homogeneity must be demonstrated through, e.g., a cluster analysis or similar procedures. The paper includes enough information to reproduce the results in general. However, the open access code of this paper does not allow to reproduce the results as it requires a number of data files which are no in open access.", "reviewer_3": "The work presented in this manuscript provides valuable insights into wind-energy-relevant extreme wind speeds over oceans. Using ERA5 reanalysis data, the study finds that 50-year-return wind speeds (U50) near hub height have increased over the past decades in many areas, which impacts the design of offshore wind turbines. The authors describe their methods of extracting a trend of U50 from the 84-year dataset, and put results into perspective of wind turbine design requirements. Related literature focuses on extreme winds from reanalysis and satellite data, but the trend of extreme winds has not yet been studied at large scales. Extrapolating the derived trends provides insights into future hub-height offshore wind extremes, supporting the planning of offshore wind energy. Below, I outline a few remarks, mainly about additional details of the methodology. General comments • In addition to the three wind turbine classes discussed in the manuscript, IEC 61400-1 (2019) includes wind turbine class T for areas with moderate wind climates that may experience very high extreme winds (e.g. tropical cyclones), with a reference wind speed (Vref) of 57 m/s. It would be interesting to extend the discussion of Vref vs. U50 for this turbine class as well. • ERA5 is known to underestimate wind speeds in general (not just derived extremes), and a recent paper showed this underestimation for strong ERA5 offshore winds in the context of wind energy (https://wes.copernicus.org/articles/9/7/2024/wes-9-1727-2024.html). This should be included in the discussion. Specific comments Introduction: o L. 47-51: Include a sentence about how U50 and Vref are connected. o L. 57: The resolution of multi-satellite products is comparable to reanalysis products. A major downside of satellite wind products is that they can only provide near-surface winds. A recent study did a similar trend analysis of offshore near-surface winds from satellite data: https://doi.org/10.1175/JTECH-D-24-0008.1 Methods: o Include a brief discussion on how the height of 100m winds in ERA 5 relates to current and future turbine hub heights. o In the IEC standards, both Vref and U50 refer to 10-minute averages. In contrast, ERA5 provides hourly instantaneous values of the 100m winds. A brief discussion about how the different time steps impact results would be helpful. o L. 460: It might be helpful to mention why some wind farms have a higher reference wind speed than the ones mentioned for the turbine classes. o L. 469: Is the CDF derived from the Weibull distribution or from (yearly) maximum values? o L. 479-497: All methods are briefly explained, except the one that is used (MLE). Please add some details about this method. Results: o When discussing the current spatial distribution (not the trend) of U50, could you mention which analysis time period this refers to? o How is Fig. 3a different from Fig S3? o It might be helpful to consistently use units of either m/s/year or m/s/decade. o L. 133-136: This motivates what has already been described in the paragraph above, if I understand correctly. o L. 143: Why is the risk classification based on the turbine class III (lowest) reference wind speed? • L. 242: In the conclusions, it might be helpful to list a global coastal average in addition to a global oceanic average, because of its relevance to wind energy. Technical comments • Fig. 2 c and d both show the trend. Could similar colors be used, maybe throughout all figures in the manuscript showing the same parameter? • L. 131: “Researchers have found” sounds awkward. • L. 304: coastal Denmark • L. 501 and below: I am not sure if “mutations” is the right word here. • Caption Fig. S1: omit “oceanic”"}, "author_response": "Response to Reviewers\nWe would like to express our deep gratitude to the editors and reviewers for their insightful\ncomments and suggestions on the manuscript, which are fair, encouraging, and constructive.\nAfter carefully studying the comments, we have made corresponding changes in the revised\nmanuscript. In this response, the corresponding revisions in the manuscript are quoted within\nthe gray box in red text (page numbers refer to the clean version of the manuscript). We believe\nthat our responses have well addressed all concerns from the reviewers.\nWrite beforehand\nWe sincerely appreciate the reviewers’ recognition of our study’s potential impact and their\nconstructive feedback. Below, we summarize our key revisions and responses to the reviewers’\nmain concerns.\n(1) Concerns about the scope of this research\nWe have revised the introduction to clearly clarify the study’s focus: assessing the historical\ntrends and spatial variability of the fifty-year return period wind speed (U ) across global\n50\noceans and its implications for offshore wind energy resilience. We have clearly introduced the\nrisky environment of offshore wind farms, threatened by tropical cyclones (TCs) and extra-\ntropical cyclones (ETCs), which are evolving under global warming. Under this background,\nquantifying the trends and spatial patterns of U is crucial for understanding the vulnerability\n50\nof offshore wind infrastructure and guiding future adaptation strategies. In addition, we give\nconcise overview of the manuscript organization to provide readers with a snapshot of this study.\n(2) Concerns about lack of related physical issues about U change\ntropical cyclone activity. The revised manuscript demonstrates a good correlation between\nrising sea surface temperatures and increasing annual maximum wind speeds (r = 0.80). Spatial\npatterns of U changes align well with intensifying tropical cyclones (particularly strong TCs)\n50\nand the poleward shift of extratropical cyclones, as evidenced by IBTrACS data analysis and\nsupported by existing literature. These findings clearly associate the observed U changes with\n50\nglobal warming-induced modifications in both TC and ETC systems, significantly advancing\nour understanding of extreme wind speed dynamics.\n(3) Concerns about reanalysis dataset underestimation and study limitations\nWe have applied spectral correction to compensate for the known underestimation of\nextreme winds in ERA5, which increases U estimates by an average of 5.56 m s-1. The revised\n50\ndiscussion acknowledges remaining conservative in cyclone-prone regions and the need for\nlocalized downscaling in high-risk areas (Supplementary Fig. 11). We have also expanded our\nevaluation to multiple turbine hub heights (80-200 m) to address concerns about single-height\nanalysis, demonstrating that risk exposure escalates substantially with increasing hub height\n(Supplementary Fig. 12). Adding these discussions enhance the comprehensiveness of this\nstudy and highlight directions for future research.\nWe hope that our revisions effectively address all concerns raised by the reviewers. Please\nsee details in the point-by-point response below.\nResponse to Reviewer #1\nReviewer Point P 1.1—This paper conducted a detailed analysis of the spatial variability of\n50-year-return-period (U_50) high extreme winds over the Earth's oceans to attempt to\ndocument their likely risk to offshore wind energy infrastructure.\nWhile a global atlas of U_50 is crucial and the results and the attempt are novel, the manuscript\nis confused about the scope of the work. The problem statement in the Introduction section is\nquite jumbled. Still, to the best of my understanding, this was the ultimate problem statement\nthat the authors make: \"While reanalysis datasets, such as ECMWF’s ERA5, provide valuable\nhistorical wind speed records dating back to 1940, previous studies have remained limited to\nassessing U50 without considering whether significant changes have occurred in the context of\nclimate change\". This makes the reader think that the paper will analyze U_50 from the\nreanalysis record (which it does) and connect it to recent climate change (which it doesn't, even\nafter claiming it to be the shortcoming of earlier studies).\nReply:\nWe sincerely thank the reviewer for the detailed and constructive comments. In response\nto the fundamental issues you raised, we have completely restructured the Introduction and\ndiscuss the relationship between U change and climate change in Result.\nand highlight the challenging environmental conditions they face. We then discuss the evolving\npatterns of tropical and extra-tropical cyclones, emphasizing their strengthening trends and\npotential impacts on wind infrastructure. And then, we introduce the concept of U , its role in\n50\nwind turbine design standards, and the existing challenges in its estimation due to limited\nobservational datasets.\nThe key question we address is: “However, the pattern and magnitude of U changes\n50\nremain poorly understood under the changing and predominantly intensifying extreme wind\nenvironment, particularly in regions where offshore wind farms are operating or under\ndevelopment. Assessing whether current and planned turbines are equipped to withstand the\nevolving wind extremes provides essential insights for offshore wind energy planning and\noperations.”\nFinally, we present the core objectives of this study, including assessing long-term trends\nof U , risk assessment for commissioned and planning wind farms, and relate U change to\n50 50\nclimate change by examining the relationship between U trends and sea surface temperature\n50\n(SST) changes as an indicator of global warming. For details please refer the Introduction in\nthe main text on pages 3-4.\nTo investigate the relationship between U changes and recent climate change, we\n50\nanalyzed sea surface temperature (SST) as a primary climate indicator and examined its\ncorrelation with annual maximum wind speeds (AMW), which serve as a proxy for U\n50\nvariability. Our analysis reveals a strong agreement between SST and AMW variability,\nsuggesting a climate influence on extreme wind patterns. This relationship can be primarily\nattributed to global warming’s intensification of both tropical and extra-tropical cyclones. A\ndetailed discussion of these mechanisms and their implications can be found in Section\n“Attribution of U trends”.\nextreme high winds using one dataset for one height and then uses that result (without providing\nany justification or uncertainty estimate) to project the historical risk into the future. This is a\nserious leap of faith.\nReply:\nThank you for your insightful comments. We acknowledge the limitations of analyzing U\n50\nsolely at a 100-meter altitude. To address this concern, we have added a discussion on U at\n50\ndifferent hub heights in the revised manuscript (Supplementary Fig. 12).\nDiscussions about the U at different hub heights on pages 13.\n50\nand have steadily increased over the past two decades, averaging 104.03 m in 2020(46), with\nthe tallest turbine reaching 185 m in 2024. Using power-law extrapolations, we estimate U\n50\nestimates at 80 m, 120 m, 150 m, and 200 m (Supplementary Fig. 12). Results show that U\n50\nand associated risk increase consistently with height. For instance, the proportion of grid\ncells facing Type 1 risk grows from 32.48% at 80 m to 54.28% at 200 m, while Type 3 risk\ncoverage expands tenfold when hub height increases from 100 m to 200 m. Although taller\nturbines improve energy yield and economic returns47, they also heighten vulnerability to\nextreme wind events.\nSupplementary Fig. 12. U at different hub height. a. Hub height = 80m. b. Hub height = 120m. c.\n50\nexceeding 37.5 m s⁻¹, 42.5 m s⁻¹, and 50 m s⁻¹, respectively.\nRegarding the projection of historical trends into the future, we fully recognize the\ncomplexity of the dynamical mechanisms driving extreme wind speed changes. We agree that\nextrapolating current trends without robust predictive modeling would be inappropriate. After\ncareful consideration, we have removed the relevant content from the manuscript given that this\naspect was mentioned only briefly (four occurrences) in the original text and is not central to\nour study. Future studies can develop more detailed predictive models for U by incorporating\nthis topic in the revised manuscript.\nRevised text in manuscript on page 12\nWhile future U projections remain uncertain and require full Earth System Model\n50\nevaluations, the observed intensification of U is likely to continue in tandem with warming\n50\ntrends42.These findings suggest that regions showing significant U increases warrant more\n50\ncomprehensive risk assessments to ensure the structural integrity and safe operation of\noffshore wind farms.\nReviewer Point P 1.3—The paper in the Conclusion section touches on the fact that extreme\nhigh winds are not the only factor in the decision-making for wind farms while highlighting\nother variables that might be considered. Of these factors, the one thing that could have been\neasily included was the central value (mean/median) of the WSPD distribution. If both\nmean/median and U_50 increase, it would point to a certain regime of risk versus the places\nwhere U_50 increases but mean/median is stationary. Above all, the \"suggestions\" that the\nauthors provide at the end of the study are pieced together from other studies. However,\nthere isn't a reasonable laying down of the actionable insights gained from THIS STUDY.\nReply:\nThank you for your valuable suggestions. We have carefully revisited these points and\nmade corresponding changes to make our analysis more comprehensive.\n(1) Regarding the comment “Of these factors, the one thing that could have been easily included\nwas the central value (mean/median) of the WSPD distribution. If both mean/median and U_50\nincrease, it would point to a certain regime of risk versus the places where U_50 increases but\nmean/median is stationary.”: in response, we have incorporated the changes in average wind\nspeed into our discussion of U to provide a more comprehensive understanding of both wind\n50\nresource variability and extreme wind behavior. We have added Supplementary Fig. 4 and\nexpanded the discussion in Section 3.1. Additionally, Fig. 2c has been introduced to illustrate\nmean wind speed in coastal regions, with relevant discussions included accordingly.\nResults about global mean wind speed change and U change on pages 5-6.\nmean wind speed trends to identify regions that exhibit both enhanced wind energy potential\nand heightened extreme wind risks. The ERA5 dataset reveals a modest but widespread\nincrease in mean wind speeds across most oceanic regions from 1940 to 2023, except for\nequatorial zones and the northern Indian Ocean (Supplementary Fig. 4a). Notably, increase\nin mean wind speeds frequently coincide with intensifying extreme winds, particularly in the\neastern tropical Pacific, southeastern Pacific, and southern Westerlies. In addition, ERA5\nprovides hourly 3-second gust wind speed, through which we observe a significant widening\nof the difference between gust winds and hourly mean winds (here we use 90th of difference\nas an example), suggesting more frequent sudden high-wind events (Supplementary Fig. 4b).\nHowever, discrepancies are observed in the north-central Pacific and northwest Atlantic\nwhen comparing ERA5-derived mean wind speed trends with those from altimeter\nobservations29 and the NCEI blended seawinds product30, may stem from temporal variations\nin data coverage, particularly in satellite-based products introduced after 1985.\nSupplementary Fig. 4. Changes in mean wind speed (MWS) and gust wind speed associated with\nU trends. a. Two-dimensional mapping of MWS and U trends. b. Trend analysis of the annual 90th\n50 50\npercentile of the difference between hourly mean wind speed and gust wind speed, based on ERA5 data\nfrom 1940 to 2023.\nResults about coastal mean wind speed change and U change on pages 6.\nnorthern and eastern Europe, the Taiwan Strait, Africa, South America, and Australia—show\ngreater potential for offshore wind energy development, but also face heightened risks due\nto concurrently strengthening extreme winds (Fig. 2b, c). Conversely, declining trends in\nboth mean and extreme wind speeds are detected in the Mediterranean Sea, East China, and\ncoastal India, likely associated with the weakening and poleward shift of boreal mid-latitude\nstorm intensity.\nFig. 2c.Trend of mean wind speed (MWS)in coastal regions.\nHowever, it is important to note that U represents the 98th percentile of annual maximum\n50\nwind speed, meaning its occurrence probability is extremely low. Therefore, while changes in\nwind resources are important, they are not the primary focus of this study. Other parameters\ncharacterizing extreme wind speeds require further investigation in the context of specific wind\nfarm environments. To clarify these limitations, we have outlined them in the “Discussion”\nsection.\n(2) Regarding the concern that the conclusions of this study are merely pieced together from\nprevious works without providing actionable insights: in response, we have revised the\nconclusion section to more clearly highlight the contributions of this study, specifically the\nrevealed changes in U trend and its linkage to global warming.\nThis study presents a comprehensive analysis of global extreme oceanic wind speeds (U ),\n50\ntheir long-term trends, and their potential links to global warming. Using gridded hub-height\nwind speed data from ERA5 (1940-2023) corrected for underestimation biases via spectral\ncorrection, we find that U has significantly increased across 62.85% of global coastal\n50\nregions over the past nine decades, with a global average trend of 0.016 m s-1 yr-1. Our results\nshow that over 40% of commissioned and planned wind farms in Asia and Europe are\nexposed to U levels exceeding IEC design thresholds in Asia and Europe, and more than\n50\n60% are located in regions experiencing rising U trends. The changes in U are closely\n50 50\nlinked to the evolving patterns of TCs and ETCs associated with global warming.\nConsidering the continuous warming trend, the regions with upward U trend needs more\n50\nattention to avoid damage caused by escalating extreme wind events.\nMain implication of this study on page 14\nIn summary, this study highlights a widespread increase inextreme wind speeds across global\noceans, with direct implications for offshore wind farm design, siting, and resilience\nplanning. By quantifying U trends and identifying its spatial patterns, our findings offer\n50\nactionable guidance for industry stakeholders, especially in vulnerable regions. As tropical\nand extratropical cyclones continue to evolve under global warming, proactive adaptation,\nincluding improved risk modeling, revised design standards, and robust turbine engineering\nwill be essential to safeguard offshore wind infrastructure and support its long-term\nexpansion.\nReviewer Point P 1.4—While I think the core of the study is reasonably impactful and deserves\nto be made public, the authors need to reframe the study in a way that justifies their assumptions\n(e.g., past trends continuing into the future), that they are very specific in their use of\nterminology (e.g., extreme winds - high or low?) and are cognizant about the limitations of their\nresults (e.g., only one hub-height, only one dataset, the uncertainties of that dataset, etc.). This\nwill require a significant amount of re-thinking, re-framing and rewriting, and possibly\nincluding a figure of a global map showing the spatial variability of high wind speed risks. I\nthink that such an amount of work likely won't be possible during a manuscript review. So, I\nwould advise the authors to resubmit after significant revisions.\nReply:\nWe sincerely appreciate your recognition of our study’s impact and value your thoughtful\nreview and constructive suggestions. We have carefully incorporated your feedback and made\nthe necessary revisions. Given the complexity of climate prediction and the fact that projecting\nfuture trends is not the primary objective of this study, we have removed the section that\nextrapolates current trends into the future.\nAdditionally, we have revised our title to \"Increasing extreme maximum wind speed\nchallenge offshore wind energy resilience\" to better reflect our focus on extreme high winds.\nThe Introduction also clarifies that this study specifically examines extremely strong winds.\nIn the Discussion section, we have thoroughly addressed the uncertainties associated with\nthe dataset and different hub heights, particularly highlighting the role of the spectrum\ncorrection method in mitigating the underestimation biases in ERA5. Furthermore, we have\nprovided a detailed extreme wind risk assessment and its relative trend map for commissioned\nand planned wind farms to enhance the practical applicability of our findings.\nWe believe that these revisions offer a more precise framing of our study and effectively\naddress your concerns. Thank you again for your thoughtful input.\nBelow, we provide the detailed context of the revised manuscript about limitation\ndiscussions.\nThe limitation of this work on pages 12~13\nNevertheless, this study has several limitations. The spectral correction (SC) method applied\nto AMW increases U by an average of 5.56 m s⁻¹ (Supplementary Fig. 11b), but\n50\nunderestimate in cyclone-prone regions, where original U values are retained and warrants\n50\nfurther refinement. As a result, wind risks in these regions remain conservative. For offshore\nwind farms located in cyclone-prone regions, a more detailed downscaling of extreme wind\nspeed load assessments is recommended to improve design safety. Due to the lack of\nsufficient long-term observations, direct validation of current U results was not feasible;\n50\nhowever, the SC method used here is consistent with in Larsén et al (2012), which\ndemonstrated strong agreement with in-situ measurements of AMW43, and has been\nsuccessfully applied in prior U assessments25,44,45. Previous studies employing empirical\n50\nformulas to correct reanalysis wind speed underestimate are limited in restricting temporal\nresolution to 1 hour and lack of in-situ observations exceeding 25 m s⁻¹ (30). In contrast, the\nSC method effectively refines U to a 10-minute resolution, aligning with IEC standard\n50\ndefinition.\nThis study evaluates U at a hub height of 100 m, while in practice, turbine heights vary and\n50\nhave steadily increased over the past two decades, averaging 104.03 m in 2020(46), with the\ntallest turbine reaching 185 m in 2024. Using power-law extrapolations, we estimate U\n50\nestimates at 80 m, 120 m, 150 m, and 200 m (Supplementary Fig. 12). Results show that U\n50\nand associated risk increase consistently with height. For instance, the proportion of grid\ncells facing Type 1 risk grows from 32.48% at 80 m to 54.28% at 200 m, while Type 3 risk\ncoverage expands tenfold when hub height increases from 100 m to 200 m. Although taller\nturbines improve energy yield and economic returns47, they also heighten vulnerability to\nextreme wind events.\nSupplementary Fig. 11. Example and results of the spectrum correction method. a. Spectrum of\nhourly ERA5 wind speed over a one-year period, with the frequency range [0.8 day⁻¹, 72 day⁻¹] adjusted\nto follow the f⁻⁵/³ slope. The spectrum shown in this figure is randomly selected from wind speed data at\na single grid point over one year. b. Difference in U values derived from the corrected and original ERA5\n50\ndata.\nSupplementary Fig. 12. U at different hub height. a. Hub height = 80m. b. Hub height = 120m. c.\n50\nexceeding 37.5 m s⁻¹, 42.5 m s⁻¹, and 50 m s⁻¹, respectively.\nReviewer Point P 1.5—I have attached more detailed comments in the text document.\nReply:\nThank you for your thorough suggestions on the manuscript. We have carefully addressed\neach of your comments and provided detailed responses in the following content.\nReviewer Point P 1.5.1—L. 1: Extreme can be high or low winds; but your study only deals\nwith maximum winds.\nReply: Thank you for your suggestion. To clarify our focus, we have added \"maximum\" to\nexplicitly indicate that our analysis pertains to high extreme wind speeds, ensuring alignment\nwith the study's scope. The current revised title is “Increasing extreme maximum wind speed\nchallenge offshore wind energy resilience”.\nReviewer Point P 1.5.2—L. 15: Same as title\nReply: Thanks for your kind reminder. We have revised to “Climate change is amplifying the\nintensity of extreme strong winds” to improve clarity and accuracy.\nReviewer Point P 1.5.3—L. 19-20: What data?\nReply: Thanks for your question. We have added clarifications to state that the data originates\nfrom ERA5.\nReviewer Point P 1.5.4—L. 20: So you’re accounting for fixed hub-height; but trying to\nquantify the impacts on real wind farms with many different hub-heights…\nReply: Thanks for your question. We have added a discussion on U at different hub heights\n50\nin the revised manuscript (Supplementary Fig. 12). And we have provided relevant content in\n“Reviewer Point P 1.2”.\nReviewer Point P 1.5.6—L. 23: Annual?\nReply: Thank you for your comment. The value mentioned refers to the cumulative installed\ncapacity. We have revised the expression accordingly in the updated manuscript for greater\nclarity.\nReviewer Point P 1.5.7—L. 29-30: Seems like you’ve quantified historical risk; but from the\nabstract actionable insights are not apparent to me.\nReply: Thank you for your feedback, and I apologize for any lack of clarity. In our study, we\ncompare U values with the IEC standard to assess potential risks. When U exceeds the IEC\n50 50\nthreshold for a given wind turbine class, it indicates a heightened likelihood of structural\ndamage, thereby identifying high-risk regions for offshore wind power deployment.\nReviewer Point P 1.5.8—L. 34: Annual?\nReply: Thank you for your question, we have revised the manuscript for clarify.\nRevised text on page 2\nOffshore wind energy is a cornerstone of the global transition to renewable energy, with\ncumulative capacity projected to rise from 63 GW in 2022 to nearly 494 GW by 2030\n(refs.1,2)\nReviewer Point P 1.5.9—L. 36: No winds can be low as well.\nReply: Yeah, it has been revised to “facing strong winds”.\nReviewer Point P 1.5.10—L. 40-41: How was it proved to be a direct result of global warming?\nThanks for your comment. We recognize the logical disconnect in the original statement and\nhave revised the relevant content to improve clarity and accuracy. The background of this paper\nhas been revised as:\nRevised text on pages 2~3\nRecent studies indicate that extreme wind patterns over global oceans are intensifying under\nclimate change. Strong tropical cyclones (TCs), particularly those with lifetime-maximum\nwind speeds above the 70th percentile of all recorded tropical cyclones, have shown a\nsignificant upward trend5. In addition, extra-tropical cyclones (ETCs) are shifting poleward\nin both hemisphere6, with a pronounced increase in transient eddy kinetic energy along the\nwinter storm tracks in Southern Hemisphere7 and a weakening in boreal mid-latitude summer\nstorm tracks8. Under a warming climate, high-resolution global climate models project an\nincrease in the frequency of strong TCs (Category 3 and above, with wind speeds exceeding\n50 m s-1) and a 3% rise in daily maximum wind speeds per degree of warming in high-latitude\nregions9–11. These changes are driven by enhanced latent heat flux12, stronger upper-level\nwinds13 and shifting sea surface temperature (SST) pattern14. This increasingly intense wind\nenvironment threatens the safe operation of wind turbines, throughout their multi-decade\nlifespans, potentially leading to damage, early retirements, and economic losses15. For\ninstance, Typhoon Yagi (2024), the strongest autumn typhoon on record, caused the collapse\nof six wind turbines in Hainan, China and led to over $8 million losses in damage (see\nExtended Fig. 1 and Movie S1)16, highlighting the vulnerability of offshore wind farms to\nextreme winds.\nReviewer Point P 1.5.11—L. 43-44: Intensifying hurricanes will automatically bring stronger\nextreme winds. These two statements are not mutually exclusive\nReply: Thank you for your valuable comment. We fully agree that intensifying hurricanes\ncontribute significantly to stronger extreme wind events, and acknowledge that the two\nstatements are not mutually exclusive. Indeed, studies indicate that over 80% of extreme wind\nevents are closely associated with tropical cyclones (TCs) and extratropical cyclones (ETCs).\nIn light of this, we have revised the introduction to more accurately reflect this relationship and\nenhance clarity. For further details, please refer to our response above.\nReviewer Point P 1.5.12—L. 45: So the authors were not talking of extreme winds, which can\nbe either minima or maxima; they are talking about maximum winds\nReply: Thank you for your suggestion. We have updated the title and first paragraph to clarify\nthat our focus is specifically on maximum wind speeds.\nReviewer Point P 1.5.13—L. 45: Need to be more specific. There are oceanic basins where\nwind speeds are projected to get slower.\nReply: Thank you for your suggestion. In the process of revising the introduction for improved\nclarity and conciseness, the corresponding expression has been removed.\nReviewer Point P 1.5.14—L. 52: Clearly specify “reference wind speeds” for wider audience\nReply: Thank you for your suggestions. In response, we have revised the text to explicitly\nintroduce U and V . Here is the revision in the manuscript:\n50 ref\nRevised text on page 3\nThe parameter used to characterize the maximum wind speed a turbine can endure during its\noperational lifespan is called reference wind speed (U ), which should exceed the\nref\nenvironmental fifty-year return period wind speed (U ) at turbine hub height17. Turbine\n50\ndesign standards by the International Electrotechnical Commission (IEC) categorize turbines\ninto Classes I, II, and III, with reference wind speeds of 50, 42.5, and 37.5 m s⁻¹,\nrespectively18. Therefore, U is a key parameter for characterizing extreme wind conditions\n50\nrelevant to wind turbine design.\nReviewer Point P 1.5.15—L. 54: Are you sure?\nReply: Thank you for your question. We acknowledge that the original statement may have\nbeen a little definitive. While U plays a critical role in offshore wind energy planning, research\n50\non its long-term trends and spatial variability, particularly in the context of current climate\nchange, remains limited. In response, we have revised the wording in the manuscript to reflect\nthis more accurately.\nReviewer Point P 1.5.16—L. 57: Sites?\nReply: Yeah. In the process of revising the introduction for improved clarity and conciseness,\nthe corresponding expression has been removed.\nReviewer Point P 1.5.17—L. 60-63: You earlier stated that there haven’t been many studies\nwhich have looked at this metric. Now you say that the shortcoming is that those trends were\nnot studied in the context of climate change. Presumably, that is your problem statement.\nReply: Thank you for your comment. We acknowledge that the original problem statement\nmay not have been sufficiently clear. In the revised manuscript, we have clarified this point as\nfollows:\nRevised text on page 3\nHowever, the pattern and magnitude of U changes remain poorly understood under the\n50\nchanging and predominantly intensifying extreme wind environment, particularly in regions\nwhere offshore wind farms are operating or under development. Assessing whether current\nand planned turbines are equipped to withstand the evolving wind extremes provides\nessential insights for offshore wind energy planning and operations.\nReviewer Point P 1.5.18—L. 64-66: Your study too focuses only on offshore winds; by the way,\nthis paragraph makes it seem like you’re talking about both onshore and offshore wind energy\nReply: Thank you for pointing this out. We acknowledge that the original wording may have\ncaused confusion regarding the scope of our study. We have revised the paragraph to clearly\nstate that our focus is exclusively on offshore wind energy.\nReviewer Point P 1.5.19—L. 79: Again; if winds trend toward very low values - that too is a\nproblem for wind energy harvesting\nReply: Thank you for your suggestion. We acknowledge that both very high and very low wind\ntrends can pose challenges for wind energy harvesting. However, our analysis specifically\nfocuses on trends in extreme maximum wind speeds. We have clarified this emphasis in the title\nand beginning of Introduction.\nReviewer Point P 1.5.20—L. 85: The fact that these are at 100 m altitude is relevant here.\nWhy 100 m only? Why not a simultaneous comparison of trends across heights? Maybe\nadjusting hub-height can help mitigate risk? But we’ll not know that from your results.\nThis is especially relevant as future wind turbines are projected to be designed taller.\nReply: Thank you for highlighting this important point. We agree that wind speed trends at\nvarying altitudes are highly relevant, especially given the increasing hub heights of future wind\nturbines. In response, we have added a discussion on U at different hub heights in the revised\n50\nmanuscript. Please refer to our earlier response to “Reviewer Point P 1.2” for further details.\nReviewer Point P 1.5.21—L. 94: How were these chosen? The figure caption makes it sound\nlike they were chosen as U-50 values were higher here?? Clarify better.\nReply: Thank you for your suggestion. To clarify, we have classified ocean basins into tropical\n(5–30° N/S) and extratropical (30–60° N/S) regions based on their dominant circulation systems,\nspecifically tropical and extratropical cyclones. This classification resulted in 11 sub-basins\nused in the current analysis.\nReviewer Point P 1.5.22—L. 101: Not sure what this means\nReply: Sorry for the unclear expression. In the process of revision for improved clarity and\nconciseness, the corresponding expression has been removed.\nReviewer Point P 1.5.23—L. 102-105: Confusing\nReply: Sorry for the unclear expression. Here we would like to express that the spatial\ndistribution of the magnitude represented by U align with the spatial distribution patterns\n50\nobserved in other datasets, which has been revised to avoid unclear expression.\nRevised text on page 5\nThese patterns are consistent with those observed in altimeter data26 and the reanalysis\ndataset from NCEP’s Climate Forecast System Reanalysis27.\nReviewer Point P 1.5.24—L. 107: Increase in spatially resolved U trends?\nReviewer Point P 1.5.25—L. 114: 32% is a big number\nReply: We agree with your observation. To better reflect the findings, we have adjusted the\nwording to emphasize that the majority of ocean regions exhibit an increasing trend, especially\nin the European offshore wind farm locations.\nReviewer Point P 1.5.26—L. 116-118: Seems to me like this section should also highlight\ntrends in some central value of the WSPD distribution, such as mean or median WSPD. That\nway it becomes clear which location should target overall higher capacity wind turbines; versus\nwhere only precautions for high maximum winds is needed even though the overall WSPD\ndistribution hasn’t shifted much.\nReply: Thank you for your suggestion. To enhance clarity, we have added an analysis of\nchanges in average wind speed in the following section. This ensures a more comprehensive\nunderstanding of extreme wind speed variations in areas with more abundant wind resources.\nHere is the content in the manuscript:\nRevised text on pages 5~6\nAlthough this study primarily examines extreme wind speeds, we conduct assessment of\nmean wind speed trends to identify regions that exhibit both enhanced wind energy potential\nand heightened extreme wind risks. The ERA5 dataset reveals a modest but widespread\nincrease in mean wind speeds across most oceanic regions from 1940 to 2023, except for\nequatorial zones and the northern Indian Ocean (Supplementary Fig. 4a). Notably, increase\nin mean wind speeds frequently coincide with intensifying extreme winds, particularly in the\neastern tropical Pacific, southeastern Pacific, and southern Westerlies.\nReviewer Point P 1.5.27—L. 125: Confused.\nReply: Thank you for your comment. We recognize that our original wording may have caused\nconfusion. To improve clarity, we have revised the sentence by using the term “significant” to\ndescribe the increasing trend of U . The updated sentence now reads: “Significant increases in\n50\nextreme wind speeds, defined as trends larger than 0.05 m s⁻¹ yr⁻¹, are concentrated in tropical\nand subtropical coastal zones.”\nReviewer Point P 1.5.28—L. 129: “With U50”\nReply: Thank you for your reminder. Due to content adjustments, the relative material has been\nremoved.\nReviewer Point P 1.5.29—L. 133-134: Not needed\nReply: Thanks for your suggestion, we have removed the relevant content.\nReviewer Point P 1.5.30—L. 135: You mean coastal land masses, not coastal offshore?\nI thought this whole study would only be about offshore\nReply: Thank you for your suggestion. Given the complexity of wind speed variations over\nland and to maintain the study’s focus on offshore wind speed analysis, we have removed the\nfigure and its related discussion from the manuscript.\nReviewer Point P 1.5.31—L. 146: Why not show other regions even if the number there is less?\nMaybe put that in the Supplementary Materials, but why ignore?\nReply: According to data from 4Coffshore, offshore wind farms are currently commissioned\nonly in Europe and Asia. Other regions have projects in the planning stage, and we have\nincluded relevant information in Supplementary Fig. 7-8 to ensure comprehensive coverage of\nfuture developments.\nReviewer Point P 1.5.32—L. 149: Smaller than what?\nReply: Thank you for pointing this out. The original intention was to highlight that the variance\nin this region was smaller compared to that in Asia. However, updated results now indicate a\nrelatively large variance. We have revised the corresponding expression in the manuscript to\nimprove clarity.\nReviewer Point P 1.5.33—L. 163-164: Any reason to believe in this “if” statement?\nReply: Thanks for your suggestion. We agree that extrapolating current trends without robust\npredictive modeling would be inappropriate and considering it is not our core objective of this\nstudy, we have removed this part.\nReviewer Point P 1.5.33—L. 165: Are these the only two or many such cases are there?\nReply: Since the 25km × 25km grid points are relatively large, multiple wind farms are often\ncontained within the same grid point. This pattern is clearly illustrated in Figures 3 and 4. To\nfurther clarify, we have included additional explanations on this point in the article, ensuring\nreaders fully understand the distribution of offshore wind farms.\nRevised text on page 7\nOur analysis focuses on U values and their trends at these wind farm locations. Notably,\n50\ngiven the coarse spatial resolution of 0.25° × 0.25° grid cells, multiple wind farms may fall\nwithin the same or adjacent grid cells.\nReviewer Point P 1.5.34—L. 243-245: Maybe a global map of these risk factors might be a\ngood way to summarize.\nReply: Thank you for your suggestion. To enhance clarity, we have marked coastal regions that\nexceed the extreme wind speed design standard in Figure 2a, providing a more comprehensive\nvisual representation of risked regions.\nReviewer Point P 1.5.35—L. 245: In the last 3 sections, we have been given statistic after\nstatistic without any clear sense of the potential impact on wind energy harvesting in existing\nwind farms, planned wind farms and developing countries.\nReply: We appreciate your feedback and apologize for any confusion. The primary focus of\nthis article is to examine the variations in U , which represents the 98th percentile of the annual\n50\nmaximum wind speed in a theoretical context. These wind speeds significantly exceed the cut-\nout threshold of wind turbines and have the potential to cause damage. While such extreme\nevents could lead to turbine shutdowns, their occurrence is extremely rare. Our aim is to\nhighlight turbine safety rather than the direct impact on wind energy harvesting. Additionally,\nwe have included an analysis of average wind speeds in the preceding sections to provide a\ncomprehensive view of both wind resource variations and extreme wind speed events.\nReviewer Point P 1.5.36—L. 249: What data? Over what time period? Etc\nReply: Thank you for the reminder—we have now included this information.\nRevised text on page 12\nUsing gridded hub-height wind speed data from ERA5 (1940-2023) corrected for\nunderestimation biases via spectral correction, we find that U has significantly increased\n50\nacross 62.85% of global coastal regions over the past nine decades, with a global average\ntrend of 0.016 m s-1 yr-1.\nReviewer Point P 1.5.37—L. 254: That’s a leap of faith. How do you know that?\nReply: Thank you for your question. Given the complexity of climate forecasting, which is not\nthe primary focus of our study, we have removed the relevant content.\nReviewer Point P 1.5.38—L. 255-259: At best, this study provides policy makers a sense of\nrisk from high wind speeds globally from one dataset. These lines do not naturally follow from\nyour results.\nReply: Thank you for sharing your perspective. This study offers preliminary insights into\nextreme wind speeds and their trends for offshore wind power development, especially\nidentifying regions experiencing significant wind speed increases that may require more\nattention from developers. We have refined the relevant expressions accordingly:\nRevised text on page 12\nThe increases of U are closely linked to the evolving and especially strengthening of TCs\n50\nand ETCs associated with global warming. Considering the continuous warming trend, the\nregions with upward U trend needs more attention to avoid damage caused by escalating\n50\nextreme wind events.\nReviewer Point P 1.5.39—L. 262-265: What?\nReply: Apologies for any ambiguity in our explanation. Due to factors such as grid smoothing,\nreanalysis data tends to underestimate extreme wind speeds, resulting in a conservative estimate\nof U . In our latest findings, we have applied spectral correction, which effectively mitigates\n50\nthis underestimation.\nReviewer Point P 1.5.40—L. 268-274: So this is your only statement on the uncertainties or\nlimitations of the ERA5 dataset in correctly estimating past AND FUTURE risks?\nI do not have a clear sense of how much statistical confidence to assign your results?\nReply: Thank you for your question. We have incorporated additional information on U\n50\nacross different hub heights and referenced relevant literature on spectral correction and U\n50\nassessment. Furthermore, we have refined to provide a more comprehensive discussion of the\nlimitations, aiming to enhance the persuasiveness of the article. Details please refer to the\nresponse of “Reviewer Point P 1.4”.\nReviewer Point P 1.5.41—L. 280-285: Irrelevant\nReply: Thank you for your suggestion. We have refined the relevant content to ensure focus\nand relevance.\nReviewer Point P 1.5.42—L. 285-286: Agreed. But you can’t predict future risks from your\nresults simply by saying “if the trends continue”. What if they don’t? What if they slow or\naccelerate?\nReply: Thank you for your suggestion. We have removed relative analysis.\nReviewer Point P 1.5.43—L. 288-301: All these would be true if the extreme winds were the\nonly parameter for decision making. But without considering other factors like average wind\nspeed and power output, turbine characteristics,\nReply: Yes, we have incorporated an analysis of average wind speed variations in the results\nsection. However, since the extreme wind speeds discussed in this study far exceed the cut-\nout thresholds (typically 25 m s-1) that lead to turbine shutdowns, ensuring turbine safety remains\na key factor in decision-making. Therefore, the magnitude of extreme wind speeds continues to\nbe a crucial parameter.\nReviewer Point P 1.5.44—L. 314-316: Unfortunately, much of the suggestions in the\nConclusion is derived from other studies. It is not apparent to the reader what THIS STUDY\nadds to the actionable knowledge about upkeeping old farms, risk-proofing future farms and\ninforming developing countries of future risks, as the manner of the language used seems to\nsuggest.\nReply: We apologize for including content in the discussion section that may have been\nperceived as irrelevant. We have revised this section to better reflect the unique contributions\nof our study. The core focus of this paper is to analyze the variations in U and its relationship\n50\nwith global warming, with particular emphasis on regions experiencing U growth and the\n50\npotential risks posed to the associated wind farms. We hope that the current revisions effectively\nclarify the implications presented in this study.\nResponse to Reviewer #2\nReviewer Point P 2.1—The manuscript \"Increasing extreme winds challenge offshore wind\nenergy resilience\" by Y. Zhao, Y. Tao, Y. Chen, and Z. Zeng addresses an important problem of\nshifting climatological norms under anthropogenic climate change. This study has its focus on\nthe 50 years return wind speed, U50, at 100 m above the ground. The design of this study is\njustified by the needs of the wind energy sector. The data analysis procedures are justified by\nthe recommendations of the International Electrotechnical commission. The authors of the\nstudy have reported scientifically sounded, methodologically robust, and generally noteworthy\nresults. Assessing the study from the bird-fly altitude, the study looks significant, reliable, and\nperhaps potentially useful.\nThere are however certain difficulties in the study that need resolutions or at least discussion.\nReply:\nThank you for your thoughtful feedback and constructive suggestions. We appreciate your\nrecognition of the significance and methodological rigor of our study. Below, we provide\ndetailed responses to your comments, along with corresponding revisions to the manuscript.\nReviewer Point P 2.2—The major drawback of the study is that it completely ignore any related\nphysical issues.\n(a) The study does not differentiate between winds generated in extra-tropical and in tropical\ncirculation systems. Extreme winds related to frontal system of extra-tropical cyclones have\ndifferent generation mechanism, different effects, constrains, and therefore different statistics\nas compared to winds generated by convection in tropical storms. Moreover, the selected large\nregions include both tropical and extra-tropical areas which belong to different circulation\nsystems with different directions of changes in extreme winds. To my opinion, such a physically\ncareless selection of geographical regions for the results' aggregation makes this aggregation\nless robust for practical applications.\nReply:\nThank you for your insightful comments. We acknowledge that the original manuscript\nlacked a detailed discussion of the underlying physical mechanisms driving changes in U .\n(1) Regarding concerns that “Moreover, the selected large regions include both tropical and\nextra-tropical areas which belong to different circulation systems with different directions of\nchanges in extreme winds.”: in response, we have classified ocean basins into tropical (5–30°\nN/S) and extratropical (30–60° N/S) regions, which correspond to the dominant circulation\nsystems of tropical and extratropical cyclones, respectively (Fig. 1a).\nRegional division based on climatological circulation systems on page 4~5\nTo analyze the spatial distribution of U , we classify ocean basins into tropical (5–30° N/S)\n50\nand extratropical (30–60° N/S) regions to reflect the distinct meteorological regimes of TCs\nand ETCs, respectively. The Southern extra-tropical Atlantic, Pacific and India Ocean are\ngrouped into a single South Westerlies region because they are well-connected and\nunobstructed by land, resulting in a total of 11 sub-basins (Fig. 1a).\nFig. 1a. The oceanic atlas of U wasconducted based on the MLE method based on wind speed from\n50\n1940-2023. The land regions were covered with blue masks since we mainly focused on the oceanic areas.\nThe 11 sub-basins include the Tropical Northwest Pacific (TNWP), Tropical Northeast Pacific (TNEP),\nTropical North Atlantic (TNA), Tropical South Indian Ocean (TSI), Tropical South Pacific (TSP),\nTropical South Atlantic (TSA), Extratropical Northwest Pacific (ENWP), Extratropical Northeast Pacific\n(ENEP), Extratropical North Atlantic (ENA), and South Westerlies (SW).\n(2) Regarding the concerns that “The study does not differentiate between winds generated in\nextra-tropical and in tropical circulation systems. Extreme winds related to frontal system of\nextra-tropical cyclones have different generation mechanism, different effects, constrains, and\ntherefore different statistics as compared to winds generated by convection in tropical storms.”:\nin response, we have utilized the mode of the month with the highest annual frequency to\ndifferentiate between wind speeds preliminary influenced by tropical cyclones (TCs) and extra-\ntropical cyclones (ETCs). This classification mainly accords to seasonal occurrence patterns, as\nTCs are more frequent during summer and autumn, whereas ETCs tend to be stronger in winter.\nTo further examine cyclone variability, we conducted a detailed analysis of TC intensity\nusing IBTrACS best-track data. Given the significant computational challenges associated with\nextracting ETC-related winds, we have incorporated recent literature to discuss changes in ETC\nintensity. Additionally, we examined U variations in relation to cyclone intensity changes.\nDifferentiating extreme winds from extra-tropical and tropical circulation systems on pages\n10-12\nThe observed correlation between SST and AMW can be explained by the intensification of\nstorm systems under warming climate conditions. The is because TCs and ETCs account for\nover 80% of extreme wind events11,36. Given that TCs are most active during summer and\nautumn, while ETCs are more frequent in winter37, we infer the meteorological origin of\nextreme wind events at different locations based on their monthly occurrence patterns. Our\nanalysis reveals that the majority of extreme wind events occur in winter months, suggesting\na higher probability to be associated with ETCs (Supplementary Fig. 9), which is because\nthe TCs are far less frequent than ETCs37. Regions that experience extreme winds in summer\nor autumn are primarily distributed across the North Indian Ocean, tropical western Pacific,\ntropical northeastern Pacific, and tropical North Atlantic spatial patterns that align well with\nknown TCs tracks.\nWe found the spatial-temporal patterns of cyclone changes remain consistence of U change.\nhighest lifetime-maximum wind speeds in all basins except the South Pacific (Supplementary\nFig. 10a, b). Similarly, accumulated cyclone energy (ACE), calculated as 𝐴𝐶𝐸 =\n∑𝑁∑𝜏𝑉2 (where V is the maximum wind speed of cyclone, τ is cyclone lifetime, and\n0 0 𝑚𝑎𝑥 max\nN is the total number of cyclone)38, also peaked during this recent period (Supplementary\nFig. 10c). The spatial extent of regions experiencing Category 4 and 5 cyclones (V larger\nmax\nthan 58 m s-1) has expanded by 4.3% compared to the 1980-2001 baseline period, especially\nin North Atlantic, consistent with the sharp increase in U observed in the Gulf Stream\n50\nregion (Supplementary Fig. 2). ETCs in both hemispheres are shifting poleward, with notable\nintensification over the northeastern Atlantic in the Northern Hemisphere and near 60°S in\nthe Southern Hemisphere11. Storm activities weaken in midlatitude regions, agreeing well\nwith the decreasing trend of U around the Mediterranean Sea and China’s Bohai Sea.\nthe positive lower-tropospheric potential vorticity11, contribute to strengthening cyclone\nintensity and raising AMW. For instance, TCs intensification rates are reported to be three\ntimes higher under marine heat wave (MHW) events compared to non-MHW\nenvironments12. Additionally, warming climates intensify upper-level winds and the\nmeridional temperature gradient39, further promoting the poleward shift of ETCs6. While\nfuture U projections remain uncertain and require full Earth System Model evaluations, the\n50\nobserved intensification of U is likely to continue in tandem with warming trends40.These\n50\nfindings suggest that regions showing significant U increases warrant more comprehensive\n50\nrisk assessments to ensure the structural integrity and safe operation of offshore wind farms.\nReviewer Point P 2.3—The study does not include any discussion of wind gusts. Indeed, there\nis a discussion of the spectral wind damping in ERA-5 data, but the proposed (in ref 37)\ncorrection has not been applied. I see this lacune as a significant gap in the study. Arguably,\nstrong wind gusts could be more damaging for the infrastructure than strong but steady winds.\nReply:\nThank you for your valuable suggestion. Given ERA5’s known underestimation of extreme\nwind speeds, we recognize the importance of applying a correction method. To address this,\nour study implements Larsén’s correction method to adjust the annual maximum wind speed, which\nis then integrated into U assessment. The detailed methodology is provided in the Methods\n50\nsection on page 18, and we present the comparation between the corrected and original U in\n50\nthe Discussion section on page 12.\nMethodology for annual maximum wind speed correction on page 18\nReanalysis products such as ERA5 underestimate strong winds due to spatial and temporal\naveraging effects in the numerical model, leading to reduced spectral energy at high\nfrequency ranges compared to observations, such as the 11% underestimation of annual\nmaximum wind speed in a numerical model with a grid resolution of 25 km compared to\nanemometer measurements43. Larsén et al43 developed a spectral correction (SC) method to\ncorrect the underestimation by filling the missing variability of high frequency range of\nmodel output. As illustrated in the spectral domain in Larsén et al (also see example from\nERA5 in Supplementary Fig. 11a), observed wind speeds follow a slope of f−5/3 for\nfrequencies f ~ 1 day−1, whereas model-simulated winds exhibit a steeper decay of f−3. The\nspectrum behavior f−5/3 at high frequency is also supported by many theoretical\narguments62,63. This discrepancy arises from energy removal by the model’s dissipation\nmechanisms, including filtering and damping schemes. They used observational wind data\nfrom Denmark, with observation heights varying between 10 m and 60 m and recorded every\n10 minutes. The correction is applied within a targeted frequency range [f , f ], where the\nc e\nlower bound f = 0.8 day-1 addresses the underestimated spectral energy occuring at high\nc\nfrequencies (f > 0.8 day-1), while the upper bound f = 72 day-1 corresponds to the Nyquist\ne\nfrequency of 10-min temporal resolution. Within this frequency range, the wind spectrum\nS(f) of reanalysis wind is corrected to follow the observed wind spectrum scaling of 𝑆(𝑓) =\n𝑓−5/3. Frequencies outside this range (f < f or f > f ) remain unmodified. This approach\nc e\neffectively downscales 1-hour ERA5 wind speed data to a 10-minute equivalent resolution,\nwhich is the recommended temporal resolution for U assessment in the IEC standard.\nspeed 𝑈̅ can be derived from the zero- and second-order spectral moments (𝑚 and\n𝑚𝑎𝑥 0\n𝑚 ) of the corrected spectrum:\n2\n𝑈̅ = 𝑈̅+√𝑚 √2𝑙𝑛( 1 √ 𝑚2𝑇 ) (1)\n𝑚𝑎𝑥 0 0\n2𝜋 𝑚0\nwhere 𝑈̅ represents the mean wind speed and 𝑇 is the reference time period (here 𝑇 = 1\n0 0\nyear). The i-th order spectral moment is defined as:\n𝑚 = 2∫ ∞ 𝑓𝑖𝑆(𝑓)𝑑𝑓 (2)\n𝑖 0\nwhere f represents frequency (Hz) and S(f) denotes the wind speed spectrum. For a\ncomprehensive derivation, refer to Larsén et al. (2012).\nThe discussion about SC method on page 12-13\nNevertheless, this study has several limitations. The spectral correction (SC) method applied\nto AMW increases U by an average of 5.56 m s⁻¹ (Supplementary Fig. 11b), but\n50\nunderestimate in cyclone-prone regions, where original U values are retained and warrants\n50\nfurther refinement. As a result, wind risks in these regions remain conservative. For offshore\nwind farms located in cyclone-prone regions, a more detailed downscaling of extreme wind\nspeed load assessments is recommended to improve design safety. Due to the lack of\nsufficient long-term observations, direct validation of current U results was not feasible;\n50\nhowever, the SC method used here is consistent with in Larsén et al (2012), which\ndemonstrated strong agreement with in-situ measurements of AMW43, and has been\nsuccessfully applied in prior U assessments25,44,45. Previous studies employing empirical\n50\nformulas to correct reanalysis wind speed underestimate are limited in restricting temporal\nresolution to 1 hour and lack of in-situ observations exceeding 25 m s⁻¹ (30). In contrast, the\nSC method effectively refines U to a 10-minute resolution, aligning with IEC standard\n50\ndefinition.\nSupplementary Fig. 11. Example and results of the spectrum correction method. a. Spectrum of\nhourly ERA5 wind speed over a one-year period, with the frequency range [0.8 day⁻¹, 72 day⁻¹] adjusted\nto follow the f⁻⁵/³ slope. The spectrum shown in this figure is randomly selected from wind speed data at\na single grid point over one year. b. Difference in U values derived from the corrected and original ERA5\n50\ndata.\nFurthermore, it is essential to assess whether extreme winds are primarily driven by strong\ngusts or steady winds. Since ERA5 provides 3-second maximum averaged wind speeds for each\nhour, we have incorporated an analysis of gust wind. Our findings indicate that the 90th\npercentile of the difference between gust wind and hourly average wind speed exhibits a\nsignificant increasing trend globally, pointing to the strengthening of instantaneous strong\nwinds is occurring at a significantly faster rate than the increase in average wind speeds and\nsuggesting that extreme wind events may primarily originate from sudden gusts.\nThe discussion about SC method on pages 5-6\nIn addition, ERA5 provides hourly 3-second gust wind speed, through which we observe a\nsignificant widening of the difference between gust winds and hourly mean winds (here we\nuse 90th of difference as an example), suggesting more frequent sudden high-wind events\n(Supplementary Fig. 4b).\nSupplementary Fig. 4b. Trend analysis of the annual 90th percentile of the difference between hourly\nmean wind speed and gust wind speed, based on ERA5 data from 1940 to 2023.\nReviewer Point P 2.4—When assessed against previous studies of the same authors, the results\nof this study look somewhat confusing. Z. Zeng and co-authors have previously reported a\nreversal of global wind stilling. They found that before 2010 winds over lands were getting more\ncalm. The supplement of this paper (Fig. S2) reports that extreme winds have been increasing\nall the time since 1970s. What is the cause of such an inconsistency? Tricks of statistical\nanalysis? Data pre-selection? In any case I would like to see the global maps of the mean wind\nspeed, U50, as well as their trends to be sure that the datasets still reproduce the earlier results\nof the same authors. Pay attention that Fig 3 gives U50 and U50 trends but there is something\nwith color scales. There is no red color on the colorbars for the panels (a) and (b).\nReply:\nThank you for your question. The observed global stilling and its subsequent reversal\nprimarily pertain to terrestrial surface wind speeds. In contrast, this study focuses on extreme\nwind speeds over the ocean, as wind speeds over land are generally much lower than the design\nstandards of offshore wind turbines. As stated in the introduction: “We exclude land areas from\nour analysis because U values over land are significantly lower than the IEC design thresholds,\n50\nwith a spatial average of just 22.57 m s⁻¹ (Supplementary Fig. 1).”\nRegarding offshore mean wind speeds, our analysis based on ERA5 indicates a\npredominant increasing trend over the ocean, while the opposing trends appear in the north-\ncentral Pacific and northwest Atlantic when comparing ERA5 data with altimeter observations\n(Young & Ribal, 2019) and the NCEI blended seawinds product (Saha et al., 2025). The\ndifference may be caused by different time coverage since satellite observed wind mainly\nstarting from 1985. However, the dynamical mechanisms driving wind speed changes over land\nand ocean are inherently complex, and a fully satisfactory explanation for these differing trends\nremains an open question in atmospheric science.\nAdditionally, we acknowledge your concern regarding the color scale of Figure 3. To\nemphasize the significance of regions where wind speeds exceed turbine design standards—\nspecifically, areas surpassing 37.5 m s-1, 42.5 m s-1, and 50 m s-1—we have highlighted these\nareas in pink. Below is the revised figure and legend.\nRevised figure 2a\nFig. 2a. Coastal U , where pastel pink, soft pink and deep pink represent U exceeding 37.5 m s-1, 42.5\n50 50\nm s-1 and 50 m s-1, respectively.\nRelevant Literature:\nYoung, I. R., & Ribal, A. (2019). Multiplatform evaluation of global trends in wind speed and\nwave height. Science, 364(6440), 548–552.\nSaha, K., Dash, P., Frech, J., Zhang, H. M., DiGiacomo, P., & Miller, S. D. (2025). Trends in\nsatellite-based ocean parameters through integrated time series decomposition and spectral\nanalysis. Part II: NOAA/NCEI Blended Seawinds. Journal of Atmospheric and Oceanic\nTechnology, 42(3), 281-294.\nReviewer Point P 2.5—I have problems with assessing results in Fig. 4. To my view, the color\nscale clearly shows that large majority of wind parks experience reduction (negative) U50\ntrends. Nevertheless, the authors report the positive trends, i.e., increased extremes in all\nregions. Is this related to statistical tricks? The way how the wind park sites weighted and\nselected?\nReply:\nThank you for your suggestion. We acknowledge that the original color bar in Figures 4\nand 5 may have led to misinterpretations when displaying trend changes. To address this, we\nhave revised the color bar to enhance clarity and prevent potential misunderstandings.\nAdditionally, we have refined our description of the trend change area of U to ensure more\n50\nprecise expression of our findings.\nThe wind farm locations are sourced from 4C Offshore, and we extract the U values and\n50\ncorresponding trend changes from the grid points where these wind farms are situated.\nRevised figure 3\nFig. 3. The U and trend of U in fully commissioned wind farms. a. Europe, b. Asia. The solid-\n50 50\ncolored dots represent the locations of wind farms in Asia and Europe. The colors of the dots represent\nthe magnitude of the trend, and the size of the dots represents the magnitude of U in each wind farm.\nrespectively.\nThe U and trend change for planning wind farms\n50 50\ndots represent the locations of wind farms in Asia and Europe. The colors of the dots represent the\nmagnitude of the trend, and the size of the dots represents the magnitude of U in each wind farm.\nrespectively.\nReviewer Point P 2.6—Finally, the part related to country scale analysis demonstrated the\nweakest relation to the physics and dynamics of the atmosphere. E.g., Turkey has two coastlines\n- one in the Mediterranean and another in the Black Seas. They are characterized by different\nwind climates. Mixing them together gives non-observable statistical mean. I would recommend\nto exclude this so-called country-scale aggregation of U50 from the paper entirely.\nReply:\nThank you for your suggestion. We acknowledge that national-level analysis may not fully\ncapture the dynamic atmospheric mechanisms influencing U trends. However, given the\n50\nfinancial and technical constraints that many developing countries face in offshore wind power\ndevelopment, providing detailed insights into U values and their trends at the country level\n50\ncan serve as a valuable reference for guiding their energy planning efforts.\nAmong the nine countries analyzed in this study, only Colombia and Turkey have coastlines\nbordering different oceanic basins. To ensure accuracy, we have conducted individual\ndiscussions of U values for the Caribbean Sea and Pacific Ocean regions in Colombia, as well\n50\nas the Mediterranean Sea and Black Sea regions in Turkey. I hope this adjustment effectively\naddresses your concerns while ensuring the study remains valuable for offshore wind power\nplanning in developing countries.\nRevised content of analysis of U in developing countries on pages 9-10\ncoastal grid cells showing increasing U trends. In Turkey, U trends exhibit clear regional\n50 50\nvariations, with significant downward trend observed near the Mediterranean Sea and\nupward trends near the Black Sea. In Sri Lanka, over 30% of grid cells show a trend greater\nthan 0.05 m s-1 yr-1 (Fig. 5a). Colombia experiences different oceanic environments due to\nits exposure to both the Caribbean Sea and the southeastern Pacific Ocean, with extreme\nwinds primarily concentrated along the Caribbean coast.\nWhen benchmarked against the reference wind speeds of wind turbines, five countries,\nincluding South Africa, Vietnam, Turkey, Colombia and the Philippines, will face extreme\nrisks (Fig. 5b). Notably, Turkey and the Philippines each have approximately 3% of their\ncoastal grid cells with U values exceeding 50 m s⁻¹ (Type 3 risk), with extreme wind\n50\nconditions in Turkey mainly occurring in the Black Sea. In addition, these high wind\nenvironment pose additional challenges for floating offshore wind energy development,\nincreased maintenance difficulty, and higher anchor costs36.\nReviewer Point P 2.7—Despite of the noted drawbacks and lack of physics in the analysis, I\nconsider this study as strong enough to recommend its publication after the required revision.\nThe analysis supports the conclusions, but special reservations are needed when the country-\nscale conclusions are presented. There are no significant flaws as such but more careful\nselection of statistically and dynamically homogeneous regions is needed in the revisited\nversion. Statistical and dynamical homogeneity must be demonstrated through, e.g., a cluster\nanalysis or similar procedures.\nReply:\nThank you very much for your valuable suggestions. Based on your feedback, we have\nredefined the regional classifications by distinguishing tropical and non-tropical areas, and\nadded a discussion of extreme wind speed variations in relation to TCs and ETCs.\nAdditionally, we have applied spectrum correction to correct ERA5’s known\nunderestimation of extreme wind speeds. For the country-scale analysis, we have refined our\napproach by addressing specific ocean regions separately, thereby improving the representation\nof regional wind dynamics.\nWe hope that these revisions have effectively addressed your main concerns and that they\ncontribute to making the study more comprehensive and reliable.\nReviewer Point P 2.8—The paper includes enough information to reproduce the results in\ngeneral. However, the open access code of this paper does not allow to reproduce the results as\nit requires a number of data files which are no in open access.\nReply:\nThank you for your suggestion. Given that the hourly ERA5 dataset exceeds 3TB, it is not\nfeasible to upload all the raw data. To ensure reproducibility, we have randomly selected a grid\npoint and extracted its corresponding raw data, allowing for the execution of all subsequent\ncode while maintaining accessibility for users.\nReviewer Point P 2.9— (Remarks on code availability):\nThe code is open access but it is useless because it requires a number of data files, which are\nnot in open access.\nReply:\nThank you again for your suggestion. To ensure accessibility, we have selected a\nrepresentative grid point as a case study, allowing readers to effectively run the calculation\nprocess for U .\nReviewer Point P 3.1—The work presented in this manuscript provides valuable insights into\nwind-energy-relevant extreme wind speeds over oceans. Using ERA5 reanalysis data, the study\nfinds that 50-year-return wind speeds (U50) near hub height have increased over the past\ndecades in many areas, which impacts the design of offshore wind turbines. The authors\ndescribe their methods of extracting a trend of U50 from the 84-year dataset, and put results\ninto perspective of wind turbine design requirements. Related literature focuses on extreme\nwinds from reanalysis and satellite data, but the trend of extreme winds has not yet been studied\nat large scales. Extrapolating the derived trends provides insights into future hub-height\noffshore wind extremes, supporting the planning of offshore wind energy. Below, I outline a few\nremarks, mainly about additional details of the methodology.\nReply:\nThank you for your thoughtful review and for recognizing our study. We have carefully\naddressed each of your suggestions individually and made the relative revisions to the\nmanuscript to enhance clarity and methodological transparency.\nReviewer Point P 3.2—In addition to the three wind turbine classes discussed in the\nmanuscript, IEC 61400-1 (2019) includes wind turbine class T for areas with moderate wind\nclimates that may experience very high extreme winds (e.g. tropical cyclones), with a reference\nwind speed (Vref) of 57 m/s. It would be interesting to extend the discussion of Vref vs. U50 for\nthis turbine class as well.\nReply:\nWe greatly appreciate your insightful comments regarding the IEC T-class wind turbine\nstandard for cyclone-prone regions (Vref = 57 m s-1). However, according to our calculations,\nonly 0.014% of the grid points exceed this threshold, and these points are not located within\ncyclone regions.\nOur latest results incorporate Larsén's spectral correction (SC) method, yet the correction\nfor cyclone-prone areas remains suboptimal. We have given relative introduction and discussion\nin the manuscript. To ensure a more precise assessment of wind turbine loads in cyclone-prone\nzones, future studies should implement a more refined downscaling model to better capture\ncyclone-intensity extreme wind.\nThe introduction of the limitation of SC method on page 19\nThe SC method has been used in different reanalysis products to assess the extreme wind\nglobally25,44,45. However, it systematically underestimates part of regions, especially the\ncyclone-prone regions, as shown by U comparisons between SC-corrected and raw ERA5\n50\ndata (Supplementary Fig. 11b). This underestimation may stem from the stronger cyclone-\nrelated signals in the high-frequency range, where the spectral slope is steeper than f⁻⁵/³,\nwhich deserves further investigation. While Larsén et al. (2022) proposed a modified spectral\ncorrection for tropical cyclones (SC-TC), our evaluation found persistent underestimation\neven after its application. Furthermore, the SC-TC method relies primarily on empirical\napproximations derived from Ott's (2005)64 Holland model simulations, rather than direct\nvalidation against observational data in tropical cyclone regions. Given these limitations, our\nstudy employs the standard SC method for annual maximum wind speed derivation. For\ncases where the corrected U remains underestimated, we retain the original ERA5 values\n50\nas conservative estimates.\nSupplementary Fig. 12. U at different hub height. a. Hub height = 80m. b. Hub height = 120m. c.\n50\nexceeding 37.5 m s⁻¹, 42.5 m s⁻¹, and 50 m s⁻¹, respectively.\nThe discussion of SC method on pages 12-13\nNevertheless, this study has several limitations. The spectral correction (SC) method applied\nto AMW increases U by an average of 5.56 m s⁻¹ (Supplementary Fig. 11b), but\n50\nunderestimate in cyclone-prone regions, where original U values are retained and warrants\n50\nfurther refinement. As a result, wind risks in these regions remain conservative. For offshore\nwind farms located in cyclone-prone regions, a more detailed downscaling of extreme wind\nspeed load assessments is recommended to improve design safety. Due to the lack of\nsufficient long-term observations, direct validation of current U results was not feasible;\n50\nhowever, the SC method used here is consistent with in Larsén et al (2012), which\ndemonstrated strong agreement with in-situ measurements of AMW43, and has been\nsuccessfully applied in prior U assessments25,44,45. Previous studies employing empirical\n50\nformulas to correct reanalysis wind speed underestimate are limited in restricting temporal\nresolution to 1 hour and lack of in-situ observations exceeding 25 m s⁻¹ (30). In contrast, the\nSC method effectively refines U to a 10-minute resolution, aligning with IEC standard\n50\ndefinition.\nReviewer Point P 3.3—ERA5 is known to underestimate wind speeds in general (not just\nderived extremes), and a recent paper showed this underestimation for strong ERA5 offshore\nwinds in the context of wind energy (https://wes.copernicus.org/articles/9/7/2024/wes-9-1727-\n2024.html). This should be included in the discussion.\nReply:\nThank you for your recommendation. The method presented in the referenced paper\nprovides an effective correction for strong winds; however, it is not directly applicable to the\nU calculations in this study due to the following limitations:\ncorrection (SC) method employed in this study allows corrections down to 10-minute intervals,\nwhich aligns with the IEC-recommended timeframe for U assessments.\nexceeding 25 m s-1, making it less representative of extreme wind conditions relevant to\noffshore wind energy applications.\nNevertheless, it can help to make our discussion more comprehensive, and we have\nincorporated a discussion of this method in the manuscript to provide a more comprehensive\nperspective on ERA5 wind speed adjustments.\nThe relative discussion in manuscript on page 13\nPrevious studies employing empirical formulas to correct reanalysis wind speed\nunderestimate are limited in restricting temporal resolution to 1 hour and lack of in-situ\nobservations exceeding 25 m s⁻¹ (30). In contrast, the SC method effectively refines U to a\n50\n10-minute resolution, aligning with IEC standard definition.\nReviewer Point P 3.4—L. 47-51: Include a sentence about how U and Vref are connected.\nThank you for your suggestions. In response, we have revised the text to explicitly clarify\nthe introduction of U50 and V .\nref\nRevised text on page 3\nThe parameter used to characterize the maximum wind speed a turbine can endure during its\noperational lifespan is called reference wind speed (U ), which should exceed the\nref\nenvironmental fifty-year return period wind speed (U ) at turbine hub height18,19. Turbine\n50\ndesign standards by the International Electrotechnical Commission (IEC) categorize turbines\ninto Classes I, II, and III, with reference wind speeds of 50, 42.5, and 37.5 m s⁻¹,\nrespectively20. Therefore, U is a key parameter for characterizing extreme wind conditions\n50\nrelevant to wind turbine design.\nReviewer Point P 3.5—L. 57: The resolution of multi-satellite products is comparable to\nreanalysis products. A major downside of satellite wind products is that they can only provide\nnear-surface winds. A recent study did a similar trend analysis of offshore near-surface winds\nfrom satellite data: https://doi.org/10.1175/JTECH-D-24-0008.1\nReply:\nThank you for sharing this paper. The use of remote sensing data to assess U has certain\n50\nlimitations, primarily due to its relatively low temporal resolution and the limited time span of\navailable satellite wind speed records. For instance, the study you referenced covers only 35\nyears, which is insufficient for conducting a robust U trend analysis. Nonetheless, the insights\n50\nfrom the referenced study regarding changes in average wind speed trends can significantly\nenrich our discussion. The relevant adjustments to the original text are as follows\nRevised text on page 6\nHowever, discrepancies are observed in the north-central Pacific and northwest Atlantic\nwhen comparing ERA5-derived mean wind speed trends with those from altimeter\nobservations29 and the NCEI blended seawinds product30, may stem from temporal variations\nin data coverage, particularly in satellite-based products introduced after 1985.\nReviewer Point P 3.5—Include a brief discussion on how the height of 100m winds in ERA 5\nrelates to current and future turbine hub heights.\nReply:\nThanks for your valuable suggestions. The U assessment in different hub heights has been\n50\nadded to the discussion part.\nDiscussions about the U at different hub heights on page 13.\n50\nhave steadily increased over the past two decades, averaging 104.03 m in 2020(46), with the\ntallest turbine reaching 185 m in 2024. Using power-law extrapolations, we estimate U\n50\nestimates at 80 m, 120 m, 150 m, and 200 m (Supplementary Fig. 12). Results show that U\n50\nand associated risk increase consistently with height. For instance, the proportion of grid\ncells facing Type 1 risk grows from 32.48% at 80 m to 54.28% at 200 m, while Type 3 risk\ncoverage expands tenfold when hub height increases from 100 m to 200 m. Although taller\nturbines improve energy yield and economic returns47, they also heighten vulnerability to\nextreme wind events.\nReviewer Point P 3.6—In the IEC standards, both Vref and U50 refer to 10-minute averages.\nIn contrast, ERA5 provides hourly instantaneous values of the 100m winds. A brief discussion\nabout how the different time steps impact results would be helpful.\nThank you for your important reminder. We acknowledge that directly using hourly ERA5 data\nfor U assessments does not conform to the 10-minute averaging standard required by IEC for\n50\nannual maximum wind speeds with a 10-minute sampling frequency. After SC correction, the\nglobal average U increases by 5.56 m s-1 compared to the uncorrected ERA5-derived values.\nSpectrum correction method on page 18\nReanalysis products such as ERA5 underestimate strong winds due to spatial and temporal\naveraging effects in the numerical model, leading to reduced spectral energy at high\nfrequency ranges compared to observations, such as the 11% underestimation of annual\nmaximum wind speed in a numerical model with a grid resolution of 25 km compared to\nanemometer measurements43. Larsén et al43 developed a spectral correction (SC) method to\ncorrect the underestimation by filling the missing variability of high frequency range of\nmodel output. As illustrated in the spectral domain in Larsén et al (also see example from\nERA5 in Supplementary Fig. 11a), observed wind speeds follow a slope of f−5/3 for\nfrequencies f ~ 1 day−1, whereas model-simulated winds exhibit a steeper decay of f−3. The\nspectrum behavior f−5/3 at high frequency is also supported by many theoretical\narguments62,63. This discrepancy arises from energy removal by the model’s dissipation\nmechanisms, including filtering and damping schemes. They used observational wind data\nfrom Denmark, with observation heights varying between 10 m and 60 m and recorded every\n10 minutes. The correction is applied within a targeted frequency range [f , f ], where the\nc e\nlower bound f = 0.8 day-1 addresses the underestimated spectral energy occuring at high\nc\nfrequencies (f > 0.8 day-1), while the upper bound f = 72 day-1 corresponds to the Nyquist\ne\nfrequency of 10-min temporal resolution. Within this frequency range, the wind spectrum\nS(f) of reanalysis wind is corrected to follow the observed wind spectrum scaling of 𝑆(𝑓) =\n𝑓−5/3. Frequencies outside this range (f < f or f > f ) remain unmodified. This approach\nc e\neffectively downscales 1-hour ERA5 wind speed data to a 10-minute equivalent resolution,\nwhich is the recommended temporal resolution for U assessment in the IEC standard.\nfrequency range [0.8 day⁻¹, 72 day⁻¹] adjusted to follow the f⁻⁵/³ slope.\nReviewer Point P 3.7—L. 460: It might be helpful to mention why some wind farms have a\nhigher reference wind speed than the ones mentioned for the turbine classes.\nReply:\nThank you for your kind reminder. The selection of a specific wind turbine model is a\ncomplex process that involves considerations of local extreme wind speeds as well as the\ninvestment costs associated with the project. Generally, regions more frequently impacted by\nTCs and ETCs—such as Japan, China, and Germany—tend to adopt wind turbine models with\nhigher reference wind speeds. We have provided further explanations on this aspect in the article.\nRevised text on page 17\nThe wind turbines in Table S1 typically have 2-8 MW capacity, with reference wind speeds\nvarying between 42.5 m s-1 and 70 m s-1, depending on the local extreme wind conditions\nand investment costs.\nReviewer Point P 3.8—L. 469: Is the CDF derived from the Weibull distribution or from (yearly)\nmaximum values?\nReply:\nThanks for your questions. The CDF is derived from fitting the yearly maximum values\nbased on maximum likelihood estimation. We have added an explanation to clarify the\nmethodology.\nRevised text on page 19\nwhere U is the annual maximum wind speed derived from the SC method.\nmax\nReviewer Point P 3.9—L. 479-497: All methods are briefly explained, except the one that is\nused (MLE). Please add some details about this method.\nReply:\nThank you for your detailed suggestion. In response, we have expanded our explanation of\nthe maximum likelihood estimation (MLE) method to provide greater clarity.\nRevised text on page 20\nAmong these, MLE considered one of the most robust non-parametric iterative approach68,69,\ndetermining parameters by maximizing the likelihood function under the assumed Gumbel\ndistribution70.\nReviewer Point P 3.10—When discussing the current spatial distribution (not the trend) of\nU50, could you mention which analysis time period this refers to?\nReply:\nThank you for your question. To clarify, U values used for trend analysis are derived from\n50\n30-year time spans, whereas all other U values referenced in the study are obtained from the\n50\nfull 84-year dataset. We have provided an explanation of this distinction in the manuscript.\nRevised text on page 4\nFor consistency, all U values reported in this study are based on the complete 84-year time\n50\nseries, except in the analysis of U trends, which applies a 30-year interval.\nReply:\nThanks for your question. Supplementary Fig. 3 in the original manuscript depicted grid\npoints along the coastline at the land-sea boundary. However, due to the complexity of wind\nspeed variations over land and to maintain the study’s focus on offshore wind speed analysis,\nwe have removed this figure and its related discussion from the paper.\nReviewer Point P 3.12—It might be helpful to consistently use units of either m/s/year or\nm/s/decade.\nReply:\nThank you for your suggestion. To ensure consistency, we have standardized all units\ndescribing wind speed changes to m s-1 year-1 throughout the manuscript.\nReviewer Point P 3.13—L. 133-136: This motivates what has already been described in the\nparagraph above, if I understand correctly.\nReply:\nThank you for your suggestion. To improve consistency and avoid redundancy, we have\nremoved the discussion to the land-based section of U .\n(lowest) reference wind speed?\nReply:\nDue to the underestimate of ERA5 to extreme wind speeds, the initial results yielded\nrelatively low U values, with only a limited number of areas exceeding the Class II threshold\n50\n(42.5 m s-1). As a result, Class III (37.5 m s-1) was initially chosen as the reference standard for\nrisk classification. However, after applying effective corrections to extreme wind speeds, we\nhave updated the risk classification criteria throughout the manuscript, including the Class I (50\nm s-1) reference wind speed.\nRevised text on page 7\nChanges in extreme oceanic winds pose increasing risks to offshore wind farms. To assess\nthese risks, we categorize wind farms into three risk categories based on the U values of\n50\nthe grid cells in which they are located, relative to IEC reference wind speed thresholds: Type\n1 (U exceeding 37.5 m s⁻¹), Type 2 (U exceeding 42.5 m s⁻¹), and Type 3 (U exceeding\n50 50 50\n50 m s⁻¹), as winds exceeding these thresholds endanger turbines in each respective category.\nReviewer Point P 3.15—L. 242: In the conclusions, it might be helpful to list a global coastal\naverage in addition to a global oceanic average, because of its relevance to wind energy.\nReply:\nThank you for your valuable suggestion. To enhance the relevance of our findings for wind\nenergy applications, we have incorporated a global coastal average in the conclusions.\nRevised text on page 12\nUsing gridded hub-height wind speed data from ERA5 (1940-2023) corrected for\nunderestimation biases via spectral correction, we find that U has significantly increased in\n50\nacross 62.85% of global coastal regions over the past nine decades, with a global average\ntrend of 0.016 m s-1 yr-1.\nTechnical comments\nReviewer Point P 3.16—Fig. 2 c and d both show the trend. Could similar colors be used,\nmaybe throughout all figures in the manuscript showing the same parameter?\nReply:\nThank you for your suggestion. To ensure clarity and consistency in visual representation,\nwe use blue to indicate a downward trend and red to indicate an upward trend for U trend\n50\nchanges.\nAdditionally, for aesthetic and readability considerations, we have maintained the current\ncolor scheme while making adjustments to Figures 3 and 4. Specifically, blue-purple now\nrepresents a decline in U , while red denotes an increase in U , aligning with the color\n50 50\nconventions used throughout the manuscript.\nReviewer Point P 3.17—L. 131: “Researchers have found” sounds awkward.\nReply:\nThank you for your suggestion. Given the new revisions to the manuscript, the referenced\ncontent has been removed, ensuring alignment with the updated structure.\nReviewer Point P 3.18—L. 304: coastal Denmark\nReply:\nThank you for catching this error. We have corrected it in the revised text on page 13.\nReviewer Point P 3.19—L. 501 and below: I am not sure if “mutations” is the right word here.\nReply:\nThank you for your suggestion. We have revised the wording to ensure clarity and accuracy.\nThe term “mutations” has been replaced with “changes” to more appropriately describe\nvariations in U under extreme wind conditions.\nIn the context of escalating extreme wind conditions in coastal and oceanic regions, exploring\nthe changes in U is imperative, as this directly impacts the seamless operation of existing\n50\noffshore wind farms and the strategic planning of new installations. (part 1)\nTo ensure the credibility of our findings, and considering the recommended minimum\nduration for U estimation is a time series exceeding 20 years, we calculate U over\n50 50\noverlapping 30-year periods. (part 2)\nReviewer Point P 3.20—Caption Fig. S1: omit “oceanic”\nReply:\nThank you for catching this error. We have removed “oceanic” from the caption of\nSupplementary Fig. 1 and made the necessary adjustments in the supplementary material to\nensure accuracy.\nReviewer Point P 3.21—(Remarks on code availability): A readme file how to use the code\nmight be helpful.\nReply:\nThank you for your suggestion. To enhance usability, we have added a README file\nproviding instructions on how to use the code, ensuring clarity and accessibility for readers.\nResponse to Reviewers\nREVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nReviewer #1 General Comments: The authors have addressed all of my previous concerns\nthoroughly and remarkably. The paper is a significant contribution to the planning of future\nwind energy infrastructure. No more comments from my part; the manuscript can be moved\nforward to be published.\n[Response] We sincerely appreciate your recognition of our efforts in addressing your previous\nconcerns, as well as your kind words regarding the significance of our work. Your insightful\ncomments have greatly contributed to improving the quality of our paper. Thank you once again\nfor your valuable contribution to our research.\nReviewer #2 (Remarks to the Author):\nReviewer #2 General Comments:\nDear Authors,\nthank you for detailed and careful consideration of my comments.\nI believe now that the technical part of the paper is ready and warrants publication.\n[Response] We sincerely appreciate your time and valuable feedback on our manuscript. Your\nconstructive comments have greatly helped us improve the technical depth and clarity of our\nwork. We have carefully addressed your concerns about the precision and clarity of expression.\nPlease find our detailed responses and adjustments in the revised manuscript. Thank you once\nagain for your insightful suggestions that contributed to enhancing our paper.\n[Reviewer #2 Comment 1] Although no one use ERA-5 for assessment of wind characteristics\nfor energy projects, it still provides a good overview of those characteristics on global scale.\nThis is an interesting part of the study.\n[Response] We sincerely appreciate your recognition of the value of using ERA5 for assessing\nextreme wind characteristics in our study. Indeed, as one of the highest-performing global\nreanalysis datasets currently available (Olauson, 2018), ERA5 provides long-term,\nhomogeneous wind data at a high spatial resolution (0.25°), making it suited for primary\nassessments for offshore wind projects. ERA5 has been successfully applied in multiple wind\nenergy studies, including long-term power generation modeling for offshore wind farms in\nEurope (Hayes et al., 2021) and offshore wind resource assessments in India (Anangapal &\nVictor, 2024). These studies demonstrate ERA5’s reliability in wind energy research. We\nappreciate your positive feedback and hope our approach aligns well with the scope of your\ncomment.\n[Reviewer #2 Comment 2] As for the climate change attribution, your approach and\nargumentation look rather strange to me. There are CMIP-6 climate model projections readily\navailable for analysis of the wind change. Obscure arguments relating wind, SST, and climate\nchange are simply not needed. I would recommend to downplay this line in your manuscript.\n[Response] Thank you for your insightful comment regarding climate change attribution.\nWe acknowledge that attributing wind speed variations through SST-wind speed correlations\ncannot directly explain the underlying dynamical mechanisms. Nonetheless, this approach\nprovides valuable insights by linking U changes with global warming. We recognize the\n50\navailability of CMIP6 model projections for attributing wind speed change to climate change;\nhowever, the limitations of these models result in underestimation of cyclone intensity and\nfrequency, as well as tracking biases in both tropical and extratropical systems, caused by coarse\nspatial-temporal resolution and challenges in parameterizing small-scale phenomena (Priestley\net al., 2020; Roberts et al., 2020). Therefore, we focus on exploring the patterns of U change\n50\nand its statistical relationship with global warming.\nFollowing your constructive suggestion, we have revised the section concerning “Attribution\nof U trends”. Specifically, we have reduced inference-based discussion and emphasized\n50\nobservational evidence. The revised content can be found on pages 10–11 of the manuscript.\nWe hope these changes have improved the clarity of this section.\n[Reviewer #2 Comment 3] The narrative and illustrative parts of the paper are however not\nready for publication. The new text is significantly worse than the original version. There are\nmany grammar, logical, and stylistic difficulties in the text. E.g. what does this text suppose to\nmean: \"Several underlying dynamic, including rising SST enhances latent heat flux and\namplifies the positive lower-tropospheric potential vorticity, contribute to strengthening\ncyclone intensity and raising AMW. For instance, TCs intensification rates are reported to be\nthree times higher under marine heat wave (MHW) events compared to non-MHW.\" How\n\"dynamic\" can \"include flux\" and \"amplify vorticity\"? How \"rates\" could be higher than\n\"events\"?\nThe text must be carefully rewritten once again before possible publication. I acknowledge that\nFigures have improved now.\n[Response] We sincerely appreciate your detailed and constructive feedback regarding the\nlanguage of our manuscript. In response to your concerns, we have undertaken a\ncomprehensive revision of the text to improve its grammatical accuracy, logical coherence, and\nstylistic clarity. Specifically, we have broken down long, complex sentences into shorter ones\nand replaced obscure phrasing with terminology that is more commonly used in academic\nwriting. For instance, the problematic sentence you highlighted has been carefully reworded to\neliminate ambiguity and enhance readability: “Several physical mechanisms may contribute to\nthese changes, including enhanced latent heat flux from rising SST6 and increased positive\nanomalies of lower-tropospheric potential vorticity11. For instance, TCs intensification rates are\nreported to be three times higher for TCs generated under marine heat wave (MHW)\nenvironments compared to those under non-MHW conditions12.”\nIn addition to this revision, we have addressed other grammatical and stylistic issues throughout\nthe manuscript. These include:\n Replacing informal or imprecise phrasing with more standard academic expressions.\n Adding “US$” to all cost assessments for clarity and consistency.\n Correcting figure labels, such as changing “Sri” to “SRI” in Figure 5a and “Philippine” to\n“The Philippines” in Figure 5b.\n Specifying the time zone (CST) for the recording time in Extended Figure 1.\nAdditionally, we invited a native English speaker to help polish the manuscript and ensure\nlinguistic precision. We hope that the revised version now meets the journal’s standards for\nclarity and readability. Once again, we are grateful for your insightful suggestions, which have\nplayed a key role in enhancing the overall quality of our work.\n[Reviewer #2 Comment 4] A more subtle problem is related to unmotivated diversity of time\nperiods used in the study. Why are there 20, 30, 50 years periods? I believe it should be\nharmonized and better explained.\n[Response] Thank you for your comment. The selection of different temporal windows (20-,\n30-, and 50-year periods) was based on careful consideration of several technical factors. For\nassessing 50-year return period wind speeds, previous studies recommend using datasets\nspanning at least 20 years to ensure sufficient sampling of high-order extremes and to capture\na representative range of climate variability (Cook, 1982). While a 50-year period would\ntheoretically align perfectly with the return period definition, we found that this duration risks\nover-smoothing the data, potentially masking important temporal variability in the time series.\nAfter this comprehensive evaluation, we ultimately selected the 30-year window, as it not only\ncaptures sufficient extreme events but also maintains temporal resolution to detect variability\nwhile providing robust trend estimation. In addition, we verified that the trend changes derived\nfrom these three different time windows showed consistent patterns (trends of 0.014, 0.016,\n0.013 in 20-yr, 30-yr, and 50-yr time window, respectively). We have expanded the\nmethodological explanation in the revised manuscript to better articulate these considerations:\n“For estimating U , previous studies recommend datasets of at least 20 years to ensure the\n50\nsufficient sampling of extreme events. While a 50-year period would theoretically align\nperfectly with the return period definition, this duration may mask temporal variability and\nprovide fewer samples for trend detection. Finally, U is calculated using overlapping 30-year\n50\nwindows shifted annually.”\n[Reviewer #2 Comment 5] All by all, I believe that a minor revision is still needed.\n[Response] We sincerely appreciate your thorough evaluation and constructive suggestions for\nimproving our manuscript. We have carefully revised the manuscript to address all concerns\nraised. Specifically, we have refined the climate change attribution methodology, substantially\nimproved the language clarity, and provided more detailed explanations for our time window\nselection. We are grateful for the reviewer’s valuable insights, which have undoubtedly\nstrengthened the quality of our work. We hope the revised manuscript now fully addresses all\nthe reviewer’s comments and meets the journal’s publication standards.\n[Reviewer #2 Comment 6] (Remarks on code availability):\nThe code is in MATLAB. This is a commercial software. We are working only with open science,\nopen data, and open software. Therefore, I did not open and did not inspect the code.\n[Response] We appreciate your commitment to open science principles. While our current\nanalysis was implemented in MATLAB, we have ensured that the complete code is well-\ndocumented and fully accessible to facilitate reproducibility. The code and detailed\nimplementation notes have been made publicly available in\nhttps://figshare.com/articles/dataset/Increasing_extreme_maximum_wind_speed_challenge_o\nffshore_wind_energy_resilience/29380535 for research purposes.\nRelevant Literature:\nAnangapal, H. B., & Victor, K. (2024). Towards sustainable energy: Integrating ERA5 data for offshore wind\nfarm planning in India. Engineering Research Express, 6(3), 035570. https://doi.org/10.1088/2631-\n8695/ad7938\nCook, N. J. (1982). Towards better estimation of extreme winds. Journal of Wind Engineering and Industrial\nAerodynamics, 9(3), 295–323. https://doi.org/10.1016/0167-6105(82)90021-6\nHayes, L., Stocks, M., & Blakers, A. (2021). Accurate long-term power generation model for offshore wind\nfarms in Europe using ERA5 reanalysis. Energy, 229, 120603. https://doi.org/10.1016/j.energy.2021.120603\nOlauson, J. (2018). ERA5: The new champion of wind power modelling? Renewable Energy, 126, 322–331.\nhttps://doi.org/10.1016/j.renene.2018.03.056\nPriestley, M. D. K., Ackerley, D., Catto, J. L., Hodges, K. I., McDonald, R. E., & Lee, R. W. (2020). An\nOverview of the Extratropical Storm Tracks in CMIP6 Historical Simulations. https://doi.org/10.1175/JCLI-\nD-19-0928.1\nRoberts, M. J., Camp, J., Seddon, J., Vidale, P. L., Hodges, K., Vannière, B., Mecking, J., Haarsma, R.,\nBellucci, A., Scoccimarro, E., Caron, L.-P., Chauvin, F., Terray, L., Valcke, S., Moine, M.-P., Putrasahan,\nD., Roberts, C. D., Senan, R., Zarzycki, C., … Wu, L. (2020). Projected Future Changes in Tropical Cyclones\nUsing the CMIP6 HighResMIP Multimodel Ensemble. Geophysical Research Letters, 47(14),\ne2020GL088662. https://doi.org/10.1029/2020GL088662\nReviewer #3 (Remarks to the Author):\n[Reviewer #3 General Comment] Thank you for addressing the concerns of all reviewers.\nWith this, I think the quality of the manuscript improved, and I have no further comments.\n[Response] We sincerely appreciate your time and valuable feedback throughout the review\nprocess. We are pleased that our revisions have addressed your concerns and improved the\nquality of the manuscript. Thank you for your constructive comments and for recognizing our\nefforts to enhance this work."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all of my previous concerns thoroughly and remarkably. The paper is a significant contribution to the planning of future wind energy infrastructure. No more comments from my part; the manuscript can be moved forward to be published.", "reviewer_2": "Dear Authors,\nthank you for detailed and careful consideration of my comments.\nI believe now that the technical part of the paper is ready and warrants publication.\nAlthough no one use ERA-5 for assessment of wind characteristics for energy projects, it still provides a good overview of those characteristics on global scale. This is an interesting part of the study.\nAs for the climate change attribution, your approach and argumentation look rather strange to me. There are CMIP-6 climate model projections readily available for analysis of the wind change. Obscure arguments relating wind, SST, and climate change are simply not needed. I would recommend to downplay this line in your manuscript.\nThe narrative and illustrative parts of the paper are however not ready for publication. The new text is significantly worth than the original version. There are many grammar, logical, and stylistic difficulties in the text. E.g. what does this text suppose to mean: \"Several underlying dynamic, including rising SST enhances latent heat flux and amplifies the positive lower-tropospheric potential vorticity, contribute to strengthening cyclone intensity and raising AMW. For instance, TCs intensification rates are reported to be three times higher under marine heat wave (MHW) events compared to non-MHW.\" How \"dynamic\" can \"include flux\" and \"amplify vorticity\"? How \"rates\" could be higher than \"events\"?\nThe text must be carefully rewritten once again before possible publication. I acknowledge that Figures have improved now.\nA more subtle problem is related to unmotivated diversity of time periods used in the study. Why are there 20, 30, 50 years periods? I believe it should be harmonized and better explained.\nAll by all, I believe that a minor revision is still needed.", "reviewer_3": "Thank you for addressing the concerns of all reviewers. With this, I think the quality of the manuscript improved, and I have no further comments."}, "author_response": ""}, {"round": 3, "reviewer_comments": {"reviewer_2": "I concluded that all my concerns had been properly addressed. I recommend this manuscript for publication."}, "author_response": ""}]

Decision letter