Weather Phenomena Research Articles

Past and present risk of spring frosts for fruit trees in the Czech Republic

Recent climate warming is reflected in the advanced onset of spring phenological phases of fruit trees and in so-called false springs. This then manifests as an increased risk of trees being damaged by late frosts in the sensitive growing stage. Based on the homogenised temperature series of 155 climatological stations divided into two altitudinal groups (below 300 m a.s.l. and 301–600 m a.s.l.), a climatological analysis of selected variables in relation to atmospheric circulation over the territory of the Czech Republic during the period 1961–2021 was performed. The number of frost days and the dates of their latest onset exhibited negative linear trends; however, only 16% and 20% were statistically significant. The onset of false spring (at least ten consecutive days with daily maximum ≥ 10 °C) was also characterised by decreasing trends, which significantly deepened after 1980. Spring frost days were particularly attributed to higher frequencies of anticyclonic circulation types such as the central anticyclone and circulation types with northeastern, eastern and southeastern airflow. False spring onset was particularly connected with the central anticyclone and circulation types with western and southwestern airflow. The increasing trends of frost risk days (days between the beginning of the false spring and the date of the latest frost with daily minimum < −1.5 °C) were statistically nonsignificant. Local series of fruit trees from southeastern Moravia proved to have significantly earlier onset of the first blossom and full blossoming phenophases. Although the damage by late frosts has been identified as an important factor contributing to low yields of fruit trees during the period 1996–2021 in the Czech Republic, other factors (e.g. other weather phenomena, diseases, pests, site, taking preventive measures against frost) must also be taken into account.

Health Smart City towards improving Public Health and Citizen Engagement

Abstract Climate change is modifying weather patterns, producing far-reaching effects on the environment, the economy and society as a whole, endangering global livelihoods, health, food and energy security, and water resources. Europe will not escape its effects, aggravating risks and threats to public health (PH). Today cities are leading the effort to tackle climate change. Cities are embracing digitalization and establishing smart cities (SC). SC should address people mobility, but also the impact of extreme weather phenomena. The degradation of the environment will foreseeably create security and health crisis that could benefit with digital technologies. The development of urban policies should be guided by evidence-based PH practices. The inclusion of PH into the pillars of the SC will contribute toward mitigating PH crises, such as the COVID-19 pandemic. Design Science Research Methodology was used to elicit a Smart Public Health City (SPHEC) framework. A set of PH and SC experts participated in the process, using diabetes as a case study. The European Green Deal served as a blueprint toward a healthier and more sustainable city. The SPHEC framework was defined by elucidating the PH functions within a SC, with the identification of a set of digital PH services that are required to support the SPHEC framework. This allows to assess the benefits obtained with the digital PH services, and provides evidence for guiding decision-making. The role of digital PH services emerges from the analysis of the framework, through the development of proper digital PH services, strengthening capacity and resilience in future climate emergencies, and motivating policy makers to endorse this challenge. Climate change is a serious threat with impact in PH and cities need to be prepared to tackle it. The use of the SPHEC framework enable to understand the role of both digitalization and the green deal in supporting public policies to tackle chronic diseases like diabetes.

Climatology and pre-convection environmental conditions of dry and wet thunderstorm high winds over eastern China

Thunderstorm high wind (THW) is defined as a kind of convective weather phenomenon with a maximum wind gust speed not less than 17.2 m·s−1 over China. It is a sudden, damaging, but common convective weather phenomenon during the warm seasons of China. By adopting a kernel density estimation (KDE) for the THWs during warm seasons (March–September) of 2010–2019 over eastern China and the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset, the dry and wet THWs usually with different mechanisms are objectively obtained, and climatology and pre-convection environmental conditions investigated. KDE shows the total precipitable water (TPW) of 38 mm can be used as the threshold for distinguishing dry THWs from wet ones. Dry THWs mainly concentrate in North China and Yunnan province, while wet ones mainly concentrate in South China. West of Hebei province has high frequencies for both dry and wet THWs. A comparison of pre-convection environmental conditions shows dry THWs mainly occur under environmental conditions with lower saturation at both the middle-lower and upper layers, while wet ones usually have relatively lower saturation at the middle-lower layers. The instability of wet THWs can be well characterized by the most unstable lifted index (MULI) and most unstable convective available potential energy (MUCAPE), while dry ones can be well depicted by the lower 500 hPa temperature caused higher temperature difference (DT85) or temperature lapse rate (TLR85) between 850 and 500 hPa. The 0–6-km vertical wind shear (SHR6) can distinguish dry THWs from wet ones better than SHR1. The typical difference can be comprehensively revealed by the representative soundings. The results provide objective references for understanding and forecasting THWs under dry and wet environmental conditions.

Extreme Weather Events and Scientific Research

The catastrophic flood by which a huge area of Emilia-Romagna region (northern Italy) was affected last May is the most recent out of a long queue of extreme weather events involving the Italian territory throughout the last 13 years. When dealing with extreme weather phenomena, representing nowadays a "minimum common denominator" worldwide, global warming should be kept into a special account, given that the highest mean temperatures on Earth in the last 140 years have been recorded between 2015 and 2022. This implies that consistent financial investments are needed to adequately cope with extreme weather events in Italy as well as in many other countries, thereby adopting an ad hoc "mind and paradigm change" and through a multidisciplinary, basic and applied, "One Health"-inspired research effort. To this aim, we should firmly keep in mind that the money sums invested in "prevention" are exceedingly lower than those spent for the "therapy" of natural disasters.

Climate Change as Dark Magic in &lt;em&gt;Miraculous: Tales of Ladybug &amp; Cat Noir&lt;/em&gt; Animation

Climate Change as Dark Magic in &lt;em&gt;Miraculous: Tales of Ladybug &amp; Cat Noir&lt;/em&gt; Animation

A Review of Recent and Emerging Machine Learning Applications for Climate Variability and Weather Phenomena

Abstract Climate variability and weather phenomena can cause extremes and pose significant risk to society and ecosystems, making continued advances in our physical understanding of such events of utmost importance for regional and global security. Advances in machine learning (ML) have been leveraged for applications in climate variability and weather, empowering scientists to approach questions using big data in new ways. Growing interest across the scientific community in these areas has motivated coordination between the physical and computer science disciplines to further advance the state of the science and tackle pressing challenges. During a recently held workshop that had participants across academia, private industry, and research laboratories, it became clear that a comprehensive review of recent and emerging ML applications for climate variability and weather phenomena that can cause extremes was needed. This article aims to fulfill this need by discussing recent advances, challenges, and research priorities in the following topics: sources of predictability for modes of climate variability, feature detection, extreme weather and climate prediction and precursors, observation–model integration, downscaling, and bias correction. This article provides a review for domain scientists seeking to incorporate ML into their research. It also provides a review for those with some ML experience seeking to broaden their knowledge of ML applications for climate variability and weather.

Measurement scales of mental health related to climate change: a scoping review protocol using artificial intelligence

IntroductionHuman actions have influenced climate changes around the globe, causing extreme weather phenomena and impacting communities worldwide. Climate change has caused, directly or indirectly, health effects such as injury and...

Simple Analytical Expressions for Steady-State Vapor Growth and Collision–Coalescence Particle Size Distribution Parameter Profiles

Abstract This study derives simple analytical expressions for the theoretical height profiles of particle number concentrations (Nt) and mean volume diameters (Dm) during the steady-state balance of vapor growth and collision–coalescence with sedimentation. These equations are general for both rain and snow gamma size distributions with size-dependent power-law functions that dictate particle fall speeds and masses. For collision–coalescence only, Nt (Dm) decreases (increases) as an exponential function of the radar reflectivity difference between two height layers. For vapor deposition only, Dm increases as a generalized power law of this reflectivity difference. Simultaneous vapor deposition and collision–coalescence under steady-state conditions with conservation of number, mass, and reflectivity fluxes lead to a coupled set of first-order, nonlinear ordinary differential equations for Nt and Dm. The solutions to these coupled equations are generalized power-law functions of height z for Dm(z) and Nt(z) whereby each variable is related to one another with an exponent that is independent of collision–coalescence efficiency. Compared to observed profiles derived from descending in situ aircraft Lagrangian spiral profiles from the CRYSTAL-FACE field campaign, these analytical solutions can on average capture the height profiles of Nt and Dm within 8% and 4% of observations, respectively. Steady-state model projections of radar retrievals aloft are shown to produce the correct rapid enhancement of surface snowfall compared to the lowest-available radar retrievals from 500 m MSL. Future studies can utilize these equations alongside radar measurements to estimate Nt and Dm below radar tilt elevations and to estimate uncertain microphysical parameters such as collision–coalescence efficiencies. Significance Statement While complex numerical models are often used to describe weather phenomenon, sometimes simple equations can instead provide equally good or comparable results. Thus, these simple equations can be used in place of more complicated models in certain situations and this replacement can allow for computationally efficient and elegant solutions. This study derives such simple equations in terms of exponential and power-law mathematical functions that describe how the average size and total number of snow or rain particles change at different atmospheric height levels due to growth from the vapor phase and aggregation (the sticking together) of these particles balanced with their fallout from clouds. We catalog these mathematical equations for different assumptions of particle characteristics and we then test these equations using spirally descending aircraft observations and ground-based measurements. Overall, we show that these mathematical equations, despite their simplicity, are capable of accurately describing the magnitude and shape of observed height and time series profiles of particle sizes and numbers. These equations can be used by researchers and forecasters along with radar measurements to improve the understanding of precipitation and the estimation of its properties.

Discriminant Analysis for Severe Storm Environments in South-Central Brazil

Abstract Severe storms produce hazardous weather phenomena, such as large hail, damaging winds, and tornadoes. However, relationships between convective parameters and confirmed severe weather occurrences are poorly quantified in south-central Brazil. This study explores severe weather reports and measurements from newly available datasets. Hail, damaging wind, and tornado reports are sourced from the PREVOTS project from June 2018 to December 2021, while measurements of convectively induced wind gusts from 1996 to 2019 are obtained from METAR reports and from Brazil’s operational network of automated weather stations. Proximal convective parameters were computed from ERA5 reanalysis for these reports and used to perform a discriminant analysis using mixed-layer CAPE and deep-layer shear (DLS). Compared to other regions, thermodynamic parameters associated with severe weather episodes exhibit lower magnitudes in south-central Brazil. DLS displays better performance in distinguishing different types of hazardous weather, but does not discriminate well between distinct severity levels. To address the sensitivity of the discriminant analysis to distinct environmental regimes and hazard types, five different discriminants are assessed. These include discriminants for any severe storm, severe hail only, severe wind gust only, and all environments but broken into “high” and “low” CAPE regimes. The best performance of the discriminant analysis is found for the “high” CAPE regime, followed by the severe wind regime. All discriminants demonstrate that DLS plays a more important role in conditioning Brazilian severe storm environments than other regions, confirming the need to ensure that parameters and discriminants are tuned to local severe weather conditions.

Tropical Cyclones and Equatorial Waves in a Convection‐Permitting Aquaplanet Simulation With Off‐Equatorial SST Maximum

AbstractTropical weather phenomena—including tropical cyclones (TCs) and equatorial waves—are influenced by planetary‐to‐convective‐scale processes; yet, existing data sets and tools can only capture a subset of those processes. This study introduces a convection‐permitting aquaplanet simulation that can be used as a laboratory to study TCs, equatorial waves, and their interactions. The simulation was produced with the Model for Prediction Across Scales‐Atmosphere (MPAS‐A) using a variable resolution mesh with convection‐permitting resolution (i.e., 3‐km cell spacing) between 10°S and 30°N. The underlying sea‐surface temperature is given by a zonally symmetric profile with a peak at 10°N, which allows for the formation of TCs. A comparison between the simulation and satellite, reanalysis, and airborne dropsonde data is presented to determine the realism of the simulated phenomena. The simulation captures a realistic TC intensity distribution, including major hurricanes, but their lifetime maximum intensities may be limited by the stronger vertical wind shear in the simulation compared to the observed tropical Pacific region. The simulation also captures convectively coupled equatorial waves, including Kelvin waves and easterly waves. Despite the idealization of the aquaplanet setup, the simulated three‐dimensional structure of both groups of waves is consistent with their observed structure as deduced from satellite and reanalysis data. Easterly waves, however, have peak rotation and meridional winds at a slightly higher altitude than in the reanalysis. Future studies may use this simulation to understand how convectively coupled equatorial waves influence the multi‐scale processes leading to tropical cyclogenesis.

ИЗМЕНЕНИЯ ГИДРОТЕРМИЧЕСКИХ УСЛОВИЙ НА ЮГЕ СИБИРИ В 1950-2020 ГГ. И ИХ СВЯЗЬ С КРУПНОМАСШТАБНЫМИ ЦИРКУЛЯЦИОННЫМИ ПРОЦЕССАМИ

The paper presents the dynamics of the hydrothermal coefficients characterizing agroclimatic resources on the territory of Siberia within the coordinates of 50°-65° N, 60°-120° E during modern climate change. The intensity, frequency, and duration of adverse weather phenomena for agriculture (the atmospheric droughts and moisture excess conditions) are analyzed. The Ped’ drought index ( S ) and the standardized precipitation evapotranspiration index (SPEI) calculated from the data of 134 weather stations and the ERA5 reanalysis are used as an indicator. In Eastern Siberia and in the west of the analyzed territory, there is an increase in aridity (a trend is 0.2-0.5 per decade). Northern regions of Kazakhstan, the Sayan Mountains, and the West Siberian Plain were characterized by a relative stability of hydrothermal conditions in 1950-2020 (statistically significant changes are not revealed). A direct linear dependence of hydrothermal conditions on large-scale circulation processes have been not detected for most cases. The greatest number of statistically significant correlation coefficients has been obtained for the drought index S and the SCAND (Scandinavia teleconnection pattern) index.

Role of meteorology in logistics planning

Meteorology affects every part of life. We need to know the weather conditions for our everyday life, but also for the work activities of the company. Transport is a very sensitive area that is very responsive to changing weather conditions. Each type of transport is sensitive to different weather phenomena. The article provides basic information about weather, its role in transportation, and the fact that hazardous weather phenomena can significantly affect the speed of delivery, safety and quality of transportation.

서리피해를 받은 잎으로 만든 홍차의 GC×GC-TOFMS 휘발성 화합물 분석

Korea suffers from unusual droughts, heavy rain, or high temperatures due to the abnormal weather phenomenon worldwide. This climate change directly impacts the occurrence of frost in spring, which is causing serious damage to the buds and leaves of tea plants that mainly grow in spring. Frost damage delays the harvest time and decreases production, leading to a decline in the income of tea-producing farmers. Therefore, it is essential to develop ways of using discarded tea leaves. This study analyzed the volatile compounds in black tea prepared from the discarded tea leaves by two-dimensional gas chromatography-time of flight mass spectrometry (GC×GC-TOFMS). The total relative content of 481 compounds was approximately 1.8 times higher in frost-damaged black tea than in regular black tea. The ketone and ester levels in frost-damaged black tea were approximately twice as high as that in normal black tea. Two types of black tea samples were well distinguished by a dependable PLS-DA model, which presented satisfied variance and cross-validated predictive capability (R2Y=0.997, Q2=0.956) without overfitting phenomena (R2=0.953, Q2=-0.266). One hundred eighty compounds with variable importance in the projection (VIP) values &gt;1.0 were applied. The result of HCA analysis, esters, and alkenes were concentrated in normal black tea, and the ketones and heterocyclic compounds were concentrated in frost-damaged black tea. These results provide basic data for improving the process of producing good products using discarded leaves caused by frost damage.

КЫРГЫЗ, АРАБ ТИЛДЕРИНДЕГИ МЕЗИЛДИК МААНИНИ, УБАКЫТТЫ, САН-ӨЛЧӨМДҮ БИЛДИРГЕН АНИМАЛИСТИК ФРАЗЕОЛОГИЗМДЕР

The article is devoted to revealing the nature of animalistic phraseology in the Kyrgyz and Arabic languages, expressing the temporal meaning, time and quantity. The above phraseological units are semantically usually associated with a period, time through action; through certain signs, properties, characteristics associated with time, the change of day and night, synonymous animalistic phraseology and their period in two languages; weather phenomena of nature; occasional participation in animal husbandry and profession; It is divided into phraseological units expressing quantity and meaning "never", and the features of the idioms of each group are identified through a specific analysis of examples taken from the two compared languages. At the same time, the reason for the commonality of phraseological units in the two languages is due to the fact that they have long lived a nomadic way of life, and animal husbandry is the main one, and it was proved that the difference is associated with the convenience of life, geographical living conditions, customs and worldview of the two peoples. Also, on examples from the works, the artistic and aesthetic properties of this type of animalistic phraseology are shown.

EVALUATION OF VISION TRANSFORMER ON WEATHER IMAGE RECOGNITION

This study implements Vision Transformer 16x16 Words model for weather images classification. Its performance is compared with other traditional convolutional neural network (CNN) architectures, namely EfficientNetB2, DenseNet201, EfficientNetB7 and MobileNetV2. These models are implemented by transfer learning techniques for classification of images. In order to ensure the comparative performance, the same hyper-parameters of their models, such as dropout rate, optimizer and learning rate are employed identically. Furthermore, the same dataset on weather image phenomena applied on all those models with the same training, validation and testing dataset of weather images classification. The dataset of 11 different image classes that are collected from different resources of weather images with various kinds of weather phenomena are employed. The test results of performance show that the Vision Transformer gives the best results at 86.20%, which is suitable for application in evaluating weather images classification problem.

Hysteresis effects of different levels of storm flooding on susceptible enteric infectious diseases in a central city of China

BackgroundRecently, attention has focused on the impact of global climate change on infectious diseases. Storm flooding is an extreme weather phenomenon that not only impacts the health of the environment but also worsens the spread of pathogens. This poses a significant challenge to public health security. However, there is still a lack of research on how different levels of storm flooding affect susceptible enteric infectious diseases over time.MethodsData on enteric infectious diseases, storm flooding events, and meteorology were collected for Changsha, Hunan Province, between 2016 and 2020. The Wilcoxon Rank Sum Test was used to identify the enteric infectious diseases that are susceptible to storm flooding. Then, the lagged effects of different levels of storm flooding on susceptible enteric infectious diseases were analyzed using a distributed lag nonlinear model.ResultsThere were eleven storm flooding events in Changsha from 2016 to 2020, concentrated in June and July. 37,882 cases of enteric infectious diseases were reported. During non-flooding days, the daily incidence rates of typhoid/paratyphoid and bacillary dysentery were 0.3/100,000 and 0.1/100,000, respectively. During flooding days, the corresponding rates increased to 2.0/100,000 and 0.8/100,000, respectively. The incidence rates of both diseases showed statistically significant differences between non-flooding and flooding days. Correlation analysis shows that the best lags for typhoid/paratyphoid and bacillary dysentery relative to storm flooding events may be 1 and 3 days. The results of the distributed lag nonlinear model showed that typhoid/paratyphoid had the highest cumulative RR values of 2.86 (95% CI: 1.71–4.76) and 8.16 (95% CI: 2.93–22.67) after 4 days of general flooding and heavy flooding, respectively; and bacillary dysentery had the highest cumulative RR values of 1.82 (95% CI: 1.40–2.35) and 3.31 (95% CI: 1.97–5.55) after 5 days of general flooding and heavy flooding, respectively.ConclusionsTyphoid/paratyphoid and bacillary dysentery are sensitive enteric infectious diseases related to storm flooding in Changsha. There is a lagging effect of storm flooding on the onset of typhoid/paratyphoid and bacillary dysentery, with the best lagging periods being days 1 and 3, respectively. The cumulative risk of typhoid/paratyphoid and bacillary dysentery was highest at 4/5 days lag, respectively. The higher of storm flooding, the higher the risk of disease, which suggests that the authorities should take appropriate preventive and control measures before and after storm flooding.

Investigating the ERA5-Based PWV Products and Identifying the Monsoon Active and Break Spells with Dense GNSS Sites in Guangxi, China

Precipitable water vapor (PWV) with high precision and high temporal resolution estimated by Global Navigation Satellite System (GNSS) is widely used in atmospheric research and weather forecasting. However, most previous works are not consensual concerning the characteristics of the PWV at different time scales and the identification of active and break spells during summ er monsoon climate in Guangxi, China. Taking radiosonde (RS) observations as reference, a strong correlation (R &gt; 0.97) exists between GNSS PWV and RS PWV with a mean root mean square error (RMSE) of 2.68 mm. The annual, seasonal, monthly, and diurnal PWV variations of three years (2017, 2018 and 2020) over Guangxi in were comprehensively investigated using 104 GNSS stations and the fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) Atmospheric Reanalysis (ERA5). The mean annual bias and RMSE between GNSS PWV and ERA5 PWV are −1.04 mm and 2.63 mm, respectively. The monthly bias and RMSE range are −0.77 to 3.87 mm, 1.32 to 4.45 mm, and the daily range is −1.41 to 1.07 mm and 1.11 to 5.02 mm, respectively. Additionally, the adopted average standardized rainfall anomaly criteria also identified 7/7/3 active spells and 5/3/7 break spells during the summer monsoon (June–September) from 2017 to 2020, respectively. During the three-year period, the daily amplitude ranges for active spells varied from 1.41 to 2.49 mm, 0.69 to 5.4 mm, and 0.88 to 1.41 mm, while the ranges for break spells were 2.45 to 6.76 mm, 1.66 to 8.17 mm, and 1.48 to 2.99 mm, respectively. The results show a superior performance of GNSS PWV compared to ERA5 PWV in Guangxi, and the maximum, minimum and occurrence time of PWV anomaly vary slightly with the season and the topography of stations. Despite temperature primarily exhibiting a negative correlation with rainfall, acting as a dampener, a positive correlation remains evident between PWV and rainfall. Therefore, densely distributed GNSS stations exhibit excellent capabilities in quantifying atmospheric water vapor and facilitating real-time monitoring of small and medium-scale weather phenomena.

A Proposed Method for Calculating the Rainfall Threshold Based on the Multi-Method to Provide Heavy Rain Disaster Impact Information

Recently, Korea has been affected by various disasters caused by climate change and the resulting changes in weather, which have been taking an increasing toll on the country. A review of weather phenomena and their socioeconomic impact identified weather disasters as one of the most damaging categories of disasters. As such, this study suggests a method for calculating the rainfall threshold to predict the impact of heavy rain. In order to calculate the rainfall threshold based on the multi-method, the entire territory of South Korea was divided into 1 km by 1 km grids, and a method for calculating the rainfall threshold was proposed by grouping them into four categories: standard watershed, urban areas, rivers, and inundation traces. This study attempted to verify the results of the rainfall threshold in standard watersheds and urban areas. The results were verified using the data from events during the heavy rain in Seoul in 2022 and 2018, the heavy rain in Busan in 2020, and Typhoon Mitag in October 2019. As a result of the verification and calculation, a rainfall threshold was found on the grid where the actual flooding damage occurred in Busan, where heavy rain caused a large amount of urban flooding in July 2020. The application of the rainfall threshold on the grid caused enough damage to flood vehicles. After this application, it was found that flooding of more than 0.2 m affected vehicles. During early September in the Gangneung grid, flooding damage was caused by Typhoon Haishen, which affected traffic. In this damaged grid, it was also found that flooding of more than 0.2 m occurred according to the rainfall impact limit. In this study, since there were no quantitative data, verification was performed using qualitative data such as news and SNS. Therefore, quantitative verification methods using flooding sensors and CCTVs need to be carried out in the future. After verification using qualitative data, we found that the time when the actual flooding damage occurred and the flooding patterns were well ascertained. The rainfall threshold calculation method and the rainfall prediction information developed in this study are expected to be applicable to impact forecasting, which can provide people affected by heavy rainfall with information on how the rainfall will affect them, as well as simple rainfall forecasts.

Diagnosing the relationship between the Madden–Julian Oscillation and United States severe convective weather outbreaks

AbstractThe Madden–Julian Oscillation (MJO) is the leading intraseasonal mode of tropical variability and has been connected to influences in extratropical weather. Recently, the MJO has been gaining traction as a useful teleconnection for extending severe weather outbreak forecast lead time. However, the MJO's intraseasonal, planetary‐scale is far from the meso‐ or micro‐scale severe weather phenomena, making identifying links between the two more difficult. In our study, we use severe weather outbreaks comprising all three main hazards (wind, hail and tornadoes), which are typically linked to a synoptic‐scale system. Outbreaks are devised using kernel density estimation and were used to determine spatial shifts for each of the eight phases of the MJO in each month. A total of 15 months/phases indicated a significant spatial shift. In the spring and summer months (March–August), southerly shifts are more likely to occur in MJO phases 7–8 and 1–3, while northerly shifts are more likely in phases 4–6. In the cool season (September–February), only 3 month/phase groups indicated significant shifts, two shifting west and one north. Composite analysis showed the likely link between the MJO and these outbreak shifts was rooted in Rossby wave breaking and generation of a Rossby wave source induced by the barotropic vorticity advection from the MJO convection. These resulting wave breaks and wave trains led to changes in the storm track (amplitude and location) that resulted in a more favourable synoptic‐scale pattern for severe weather over the United States that deviated from the climatologically favourable region.

How does Mei-yu precipitation respond to climate change?

Mei-yu is an important weather phenomenon in the middle-lower Yangtze River valley (YRV) region. This study investigates the changes in the characteristics of Mei-yu under global warming and the potential reasons based on observation and reanalysis data during 1961-2022. Notable increasing long-term trends are detected in the number of days without rainfall (NDWOR), the intensity of rainfall events, and the frequency and intensity of extreme precipitation events (EPEs) in the YRV region during the Mei-yu period (15 June-10 July) over past decades. The increasing trend in NDWOR is attributed to decreased relative humidity over land surface and a longer time for the air to be replenished with moisture after rainfall events in a warming climate. The increasing trends in the intensity of rainfall events and frequency/intensity of EPEs are attributed to the strengthened transient water vapor convergence and convection in the atmosphere under global warming. Furthermore, the response of Mei-yu to 2°C of global warming with respect to the pre-industrial climate is analysed using CMIP6 models. The results suggest that the NDWOR, intensity of rainfall events and frequency of EPEs will increase in the YRV region during the Mei-yu period under the 2°C warming scenario, which implies a more challenging climate risk management in the future. Overall, the intensity of rainfall events during the Mei-yu period has the most significant response to climate change in observations and projections. The model results have a relatively large uncertainty.