Weather Research Articles

Cloud characteristics and their role in the 2024 United Arab Emirates extreme rainfall events

ABSTRACT Intensified by climate change, extreme rainfall events are frequently increasing and have become severe particularly in tropical and subtropical regions. On 16 April 2024, the United Arab Emirates (UAE) experienced a record-breaking flood event, characterized by ~ 250 mm of rain within a 24-hour period and surpassed the previous record of 1949. Later, on 2 May 2024, another rainfall event occurred but with low rainfall (~20 mm in 12 hours). These consecutive incidents raised a need to understand convective mechanism, so present study focus to analyse it using various cloud properties over Dubai. Our findings inferred that the April event was mostly driven by extensive cloud such as cumulonimbus which stretched up to ~ 16 km, with prominent features of high optical thickness (~150), small-sized cloud droplets (~23.89 µm), and considerable liquid water content (~2185 g/m−2). Further, we found high (~400–1800 J kg−1) and comparatively low (~0–700 J kg−1) potential energies, which trigerred convection on April 16 and May 02 respectively. Thus our study gained insights related to occurrence of convective activities at regional level using atmospheric and cloud parameters. These outcomes can be used as an input in climate models to improve weather patterns even in arid environments like Dubai, where cloud seeding is a regular practice.

SISTEM PAKAR DIAGNOSIS PENYAKIT KULIT MENGGUNAKAN METODE FORWARD CHAINING DENGAN PYTHON

Abstract. Skin is the outer layer of the body that functions to protect the inside. Like other parts of the body, the skin is also susceptible to disorders or diseases. Currently, the need for healthy skin is increasing due to pollution, free radicals and hot weather. Increased awareness about the importance of healthy skin encourages people to pay more attention to skincare. However, delays in identifying the type of disease and lack of knowledge about prevention can lead to more severe skin conditions, such as cancer. An expert system is an information system that utilizes knowledge and decision-making methods from experts in a particular field. Therefore, the author developed a system to help people with early diagnosis of skin diseases using the forward chaining method and the Python programming language. This implementation succeeded in creating an expert system capable of diagnosing skin diseases using the forward chaining method and Python. Based on testing each existing rule, the expert system succeeded in providing a diagnosis, description, solution or medicine, as well as a picture of the disease based on the symptoms reported by the user.

Legitimacy under pressure: Energy firms’ expansion in countries with weak environmental performance

Environmental pollution and climate change have become alarming global issues in the process of rapid economic growth and accelerated internationalization. Due to the environmentally sensitive nature of energy firms, maintaining their legitimacy in the international expansion process, especially in countries with weak environmental performance, is a largely underexplored area in the existing literature. Building on legitimacy as a theoretical perspective, this study examines energy firms’ international expansion patterns when facing environmental pressure. We analyze a dataset of 2134 cross-border mergers and acquisitions conducted by energy firms between 1992 and 2019 to examine the impact of host-country environmental performance, encompassing environmental health (i.e., environmental conditions that affect human well-being) and climate change (i.e., variations in weather patterns), on their expansion. We also investigate the boundary conditions underpinning this relationship. We find that firm-level internalization capability and country-level diplomatic relations make energy firms appear legitimate to their internal and external audiences, respectively. The findings bring fresh insights to the literature on international expansion under environmental threats, enrich the legitimacy perspective, and outline practical implications for firms to preserve and enhance legitimacy for international growth. Additionally, we discuss important policy implications for governments to strengthen regulatory standards on environmental issues in support of sustainable world development.

Examining the Impact of Climate Change on Agriculture Products in the Province of Jawzjan (Case Study: Certain Villages in Sheberghan City)

A region's weather and climate patterns are impacted by climate change over an extended period of time. Rising temperatures, altered precipitation patterns, rising sea levels and other modifications to meteorological conditions are some of these changes. Numerous facets of existence, including the environment, economy, and human communities, may be significantly impacted by climate change. Significant repercussions of climate change are possible, particularly in the agricultural sector. The production of food crops may be impacted by these changes because agricultural goods are dependent on local climate and weather. Pests and plant diseases may also become more prevalent due to climate change. Accordingly, higher temperatures and changed precipitation patterns can foster the growth of pests and illnesses, which will lower agricultural yields. Investigating how climate change affects agricultural products in Sheberghan City, Jawzjan Province, is the goal of this study. A questionnaire was used in fieldwork to perform this study. Six communities inside Sheberghan City—Toka, Qowchin, Jalalabad, Seshanba, Nokar Abad, and Afghan Tapa—were given 100 questionnaires in all. Four different types of questionnaires were produced and designed for the survey portion of this study. Of the 100 responders, 26 worked as farmers, 37 as gardeners, 8 as teachers, and 29 as traders in agricultural products. Using the SPSS26 program, the research was analyzed. The results showed that agricultural goods are significantly impacted by climate change, highlighting the significance of agriculture and agricultural products in the economy. The research findings indicate that the utilization of contemporary technology, such as smart irrigation systems and smart systems, can be one of the elements in growing agricultural products, and that the development of climate-adaptive agricultural methods can boost agricultural products. Farmers' interest can also be raised by government encouragement and assistance. The general conclusion that can be drawn from looking at the aforementioned elements is that agricultural products have been impacted by climate change and the creation of farmers' techniques for adapting to it.

MITIGATING THE URBAN HEAT ISLAND EFFECT: The Thermal Performance of Shade-Tree Planting in Downtown Los Angeles

The intensifying urban heat island (UHI) effect presents a growing challenge for urban environments, yet there is a lack of comprehensive strategies that account for how multiple factors influence tree-cooling effectiveness throughout the year. While most studies focus on the effects of individual factors, such as tree shading or transpiration, over specific time periods, fewer studies address the combined impact of various factors—such as seasonal variations, building shading, transpiration rates, tree placement, and spacing—on tree cooling across different seasons. This study fills this gap by investigating the thermal environment in downtown Los Angeles through ENVI-met simulations. A novel tree-planting strategy was developed to enhance cooling performance by adjusting tree positions based on these key factors. The results show that the new strategy reduces Universal Thermal Climate Index (UTCI) temperatures by 2.2 °C on the hottest day, 0.97 °C on the coldest day, and 1.52 °C annually. The study also evaluates the negative cooling effects in colder months, demonstrating that, in cities with climates similar to Los Angeles, the benefits of tree cooling in hot weather outweigh the drawbacks during winter. These findings provide a new method for optimizing tree placement in urban planning, contributing to more effective UHI mitigation strategies.

Assessment of weather-driven travel behavior on a small-scale docked bike-sharing system usage

Bike-sharing systems play a crucial role in encouraging sustainable transportation, and understanding their usage characteristics is essential for enhancing their contribution to urban mobility. This research seeks to investigate how weather conditions impact the utilization of a small-scale docked bike-sharing system. The study employed Generalized Linear Mixed Effects (GLME) models to analyze interactive events, using categorized weather parameters to represent various weather conditions. Several models were developed to comprehensively understand distinct travel behaviors and identify significant weather variables affecting the frequency of bike trips for transportation and leisure purposes. The findings reveal that rain had a significant deterrent effect on leisure cycling, particularly on weekdays. Cold and hot weather conditions exhibited a more pronounced impact on weekday bike trips, while weekend bike trips appeared to be less influenced by weather variables. The fall season was found to be the least favorable for leisure trips, while winter was determined to be the most unfavorable for transportation trips. Furthermore, hot days in the summer season negatively impacted bike usage only on weekdays. These insights have important implications for the development of a more resilient bike-sharing system, particularly in small-scale contexts. They provide valuable recommendations for tailored strategies to mitigate the impact of adverse weather conditions, thereby fostering an increase in usage of bike-sharing systems for both leisure and transportation purposes.

Breaking the rain barrier: A novel approach in image processing with AMGR‐Net

AbstractImage quality is significantly impacted by rain, posing challenges in fields like surveillance, autonomous driving, and outdoor robotics. The field of image deraining, particularly for single image, has attracted considerable attention to improve image clarity in inclement weather. To overcome the inherent complexity of the single‐image rain removal task, we proposed a novel architecture of the attention mechanism and gated recurrent network (AMGR‐Net) that combines spatial and channel attention mechanisms with gated recurrent units. AMGR‐Net contains multiple modules, each of which uses convolution kernels and attention mechanisms to enhance feature extraction. AMGR‐Net demonstrates superior performance over state‐of‐the‐art methods in both synthetic and real‐world image datasets, as evidenced by higher peak signal to noise ratio and structural similarity index measurement scores. The integration of spatial attention significantly enhances feature expression, enabling more effective rain streak removal and detail preservation. Furthermore, this method also shows promising results in the application of stripe noise removal from meteorological satellite cloud images.

Effect of temperature on seasonal wind power and energy potential estimates in Nordic climates

AbstractA major obstacle standing in the way of full‐scale adoption of renewable energy sources is their intermittency and seasonal variability. To better understand the power generation dynamics, the effect of air density due to temperature on power and energy generation figures was modelled. The model uses historical ERA5 data and considers changes in weather patterns in a subarctic climate where seasonal changes are most pronounced. The power generation figures of using a mean and a dynamic air density value were compared and the results show that power generation estimates may be under‐ and overestimated by on average 5% up to 10% in winter and summer, respectively. This can have implications for the sizing of power transmission infrastructure and energy storage in both on‐grid and off‐grid applications as well as power availability. The topic is highly relevant in the Nordic countries where roughly 10% of new global added wind capacity is installed annually.

Generation and evaluation of energy and water fluxes from the HOLAPS framework: Comparison with satellite-based products during extreme hot weather

Improving our understanding of the energy and water exchanges between the land surface and the lower atmosphere (i.e. land–atmosphere interactions), and how climate change may affect them, is crucial to predict changes in temperature and precipitation extremes. Observations of energy and water fluxes at the land surface are typically retrieved from the eddy covariance method, which presents limitations related to spatial and temporal gaps, and the non-closure of the energy and water balances. Meanwhile, soil moisture (SM) products derived from satellite data have been widely used at regional and global scales, but they are limited to capture only surface soil water content and variations. The combination of remote sensing (RS) data and modelling frameworks is called to be the solution to improve the spatial coverage and vertical resolution of land–atmosphere interactions data, ensuring the energy and water balance closure. Here, we explore the combination of remote sensing and meteorological data with a physical-based modelling framework, the High resOlution Land Atmosphere Parameters from Space (HOLAPS). We used HOLAPS to produce hourly consistent estimates of energy and water fluxes over Europe at 5 km resolution. HOLAPS and other satellite-based evapotranspiration and sensible heat flux products from the literature are evaluated against the water balance method and eddy covariance measurements. HOLAPS SM estimates together with other RS-modelling products are also evaluated against ground-based measurements at the surface and in the root zone. The evaluation of HOLAPS ET estimates show similar performance to the other products with Kling–Gupta efficiency (KGE) ¿ -0.41 in comparison with eddy covariance measurements from FLUXNET in all seasons but in boreal winter. The simulation of H is more uncertain than for ET with KGE values ranging from -2.5 to 0.8 along the products and stations at monthly scales. HOLAPS reaches slightly better results than the rest of ET and H products at daily scales in summer (KGE ¿ 0.3 for ET and KGE ¿ 0.0 for H) and during hot conditions (KGE ¿ 0.2 for ET and KGE ¿-0.2 for H), while the state-of-the-art products show KGE ¿ 0.1 for ET and KGE ¿ -0.41 for H in summer and KGE ¿ -0.1 for ET and KGE ¿ -0.6 for H during hot conditions. All products evaluated here yield a reasonable performance (KGE ¿-0.41 at most sites) in simulating SM at the surface and in the root zone. Our results expose the need for further investigating and improving product performances during extreme conditions. The good performance of HOLAPS together with its inherent advantages (RS data driven, high temporal and spatial resolution, spatial and temporal continuity, soil moisture at different depths and long-term consistent evapotranspiration and sensible heat flux estimates) support its use for agricultural and forest management activities as well as to study land–atmosphere interactions based on Earth Observations.

AI to create personalised health responses to air pollution

AI to create personalised health responses to air pollution Professor Fan Chung, Professor Christopher Pain and Claire Dilliway from Imperial College London, walk us through the exciting use of artificial intelligence to create personalised health responses to air pollution and invite you to get involved. 99% of the world’s population breathe indoor and outdoor air which fails to meet the WHO guideline limits for air pollution, taking approximately 7 million lives prematurely per year. (1) The impact of air quality on health is, therefore, a matter of concern for us all. Climate change brings yet more challenges, causing weather patterns to change, leading to more frequent extreme weather events that can increase air pollution, such as heatwaves, wildfires and drought. (2) National governments seek to address these problems, but this process will take time.

Glacial interglacial variations in the natural iron fertilization during the low sea ice periods along the eastern continental margin of Antarctica

The Southern Ocean sequesters atmospheric CO2 through biological pumps, though its driving factors are debated. Modern productivity is regulated by natural iron fertilization from micronutrient influx through dust, regeneration, and Antarctic glaciers and sea ice melting (ice melt). The productivity along the eastern Antarctic continental margin was low during the last glacial period and gradually increased through the deglacial to Late Holocene, marked by distinct productivity peaks. The micronutrients also varied similarly, with reduced glacial influx and an increase in the Holocene, which may cause productivity peaks. Therefore, the coherence of enhanced productivity and micronutrient influx is considered as enhanced iron fertilization period. The productivity peaks declined within ∼1.5 kyr, mostly due to the Ekman transport and sea ice formation. Along with ice melt, the independent weathering pattern that responds with glacial- interglacial ice volume changes suggest the source of micronutrients is not terrigenous. Shelf interaction of oceanic currents can influence the water column nutrient stock significantly, while its utilization occurs during reduced ice periods (low sea ice and high glacier melting) for productivity. Therefore, enhanced natural iron fertilization periods are considered as ice low periods that occurred in a periodicity of 2 kyrs, at ∼7.5 kyr BP, ∼5.5 kyr BP, ∼4 kyr BP, ∼ 2.5 kyr BP, and ∼0.5 kyr BP, likely due to the combined effects of atmospheric and oceanographic factors driven by the high amplitude regional temperature variability during the mid to late Holocene.

Weather Prediction Using Machine Learning Classifiers

This study evaluates machine learning algorithms to increase weather predicting accuracy. However, traditional NWP models are computationally time-consuming and may fail to capture the nonlinear complexities inherent in meteorological observations. This research studies three machine learning algorithms—XGBoost, Gradient Boosting, and Random Forest. For the prediction of future conditions based on detailed patterns of past weather, these methods are adopted. The XGBoost model recorded an accuracy rate of 82.25% which is higher than the Random Forest and Gradient Boosting models. All models performed well in normal weather conditions such as "sun" and "rain," but were least competent at predicting less common phenomena like "drizzle," "fog," and "snow." This demonstrates the challenge of making predictions when unbalanced data sets are used in weather forecasting exercises. The incorporation of such machine learning techniques with conventional NWP models has the potential to result in improved prediction performance in various applications.

Influence of probiotic strains on growth performance, carcass characteristics and seasonal changes of growing New Zealand white rabbits.

This experiment was designed to explore how different types of probiotics affect the growth, carcass traits, and seasonal variations in growing New Zealand White rabbits (NZW). Two parallel experiments using the same strain of NZW during winter and summer, each alone from 5 to 13 weeks of age. Each experiment uses a total of 125 unsexed rabbits. These rabbits are separated into 5 groups of 25 rabbits each. Each group has five replicates, with five rabbits in every replicate. In each experiment, 1st group acting as the control group did not receive any probiotics. The 2nd was given a dose of 1ml of Bifidobacterium bifidum, the 3rd received a dose of 1ml of Lactobacillus acidophilus, and the 4th was treated with a 1ml blend of both Bifidobacterium bifidum and Lactobacillus acidophilus, and 5th group was treated with 1ml of Saccharomyces cerevisiae. Results indicated that the Bifidobacterium bifidum group had the best live body weight (LBW) values and daily weight gain (DWG). Meanwhile, during summer, the Lactobacillus acidophilus group had the best feed conversion ratio (FCR) and performance index (PI) values. Also, growing rabbits fed Lactobacillus acidophilus and Bifidobacterium bifiduim had significantly increased carcass traits during the summer and winter seasons. Furthermore, seasonal changes indicated that the Bifidobacterium bifiduim group improved LBW, DWG, and PI during summer than winter. So, it could be concluded that using Bifidobacterium bifidum can enhance rabbit growth by improving feed utilization and carcass traits, making it an effective addition to hot weather diets.

Risk, Attributable Fraction and Attributable Number of Cause-Specific Heat-Related Emergency Hospital Admissions in Switzerland.

We assessed the relationship between heat and emergency hospital admissions (EHAs) in Switzerland using clinically relevant metrics. Applying distributed lag non-linear models, we investigated temperature-admission associations between May and September 1998-2019 for various disease groups, by age class and gender. We estimated the relative risk (RR) for moderate (29°C) and extreme (34°C) daily maximum temperatures relative to disease-specific optimum temperature, and calculated attributable fractions (AFs) for hot days and the following week. We also calculated the total number of heat-related EHAs. We attributed 31,387 (95% confidence interval: 21,567-40,408) EHAs to above-optimal temperatures, 1.1% (0.7%-1.4%) of the total. Extreme temperatures increased the EHA risk for mental, infectious and neurological diseases. We observed particularly high AFs due to extreme heat for dehydration (85.9%, 95% CI: 82.4%-88.8%) and acute kidney injury (AKI, 56.1%, 95% CI: 45.3%-64.7%). While EHA risk generally increased with age, we also found high RRs for infectious diseases in children (0-15years) and AKI in young adults (15-64years). Hot weather increases the EHA risk in Switzerland. Therefore a comprehensive clinical and public health response is needed.

Impact of stochastic physics on the representation of atmospheric blocking in EC-Earth3

Abstract. Atmospheric blocking is a synoptic-scale phenomenon that consists in an obstruction of the normal easterly progression of weather patterns in the midlatitudes, leading to persistent atmospheric conditions sometimes associated with extreme weather. State-of-the-art climate models systematically underestimate winter atmospheric-blocking frequency, especially over Europe. This is often attributed to a poor representation of small-scale processes that are fundamental for the onset and maintenance of blocking events. Here, we explore how the implementation of two stochastic parameterizations, namely the stochastically perturbed parameterization tendencies (SPPTs) and the stochastic kinetic energy backscatter (SKEB) schemes, influences the representation of Northern Hemisphere winter blocking in EC-Earth3. We show that the activation of the two stochastic schemes has moderate detrimental effects on blocking representation, when assessed through a gradient reversal index. Using a zonal–blocked flow linear decomposition, we attribute such modification to changes in the mean winter atmospheric circulation, primarily manifested in a strengthening of the midlatitude jet stream and an intensification of the Hadley cell. Ultimately, an analysis of the meridional transport of zonal momentum by stationary and transient eddies reveals that these circulation differences arise from changes in tropical stationary-eddy activity. Our findings reconnect with earlier literature on similar experiments and suggest that the activation of stochastic parameterizations may require a retuning of the model to account for the resulting significant changes in the mean atmospheric circulation.

Integrating hydrological parameters in wildfire risk assessment: a machine learning approach for mapping wildfire probability

Abstract The increasing occurrence of catastrophic wildfire across the globe threatens public health, community safety, ecosystem functioning, and biodiversity resilience. Wildfire risk is closely connected to shifting climatic trends and their impacts on fuel availability and flammability. Although previous research has explored the connection between meteorological conditions and wildfire probabilities, there remains a substantial gap in understanding the influence of hydrologic drivers, such as groundwater recharge, on wildfire dynamics. Both short- and long-term variations in these variables are crucial in shaping fuel conditions, and significant changes can create environments more prone to severe wildfires. This study focuses on Santa Barbara County to examine the connection between wildfire probability and various environmental factors, including meteorological and hydrological data from 1994 to 2021, topography, vegetation, and proximity to road. Using a random forest (RF) machine learning model and fine-scale data (270 m resolution) we achieved high predictive accuracy in identifying wildfire probability. Our findings confirm the important roles of short-term meteorological conditions, such as mean precipitation 12 months and relative humidity 1 month before a wildfire event, in predicting wildfire occurrence. In addition, our results emphasize the critical contribution of long-term hydrological components, such as mean deviation from the historical normal in actual evapotranspiration and recharge in the years preceding the fire, in influencing wildfire probability. Partial dependence plots from our RF model revealed that both positive and negative deviations of these hydrological variables can increase the likelihood of wildfire by controlling fuel water availability and productivity. These findings are particularly relevant given the increasing extreme weather patterns in southern California, significantly affecting water availability and fuel conditions. This study provides valuable insights into the complex interactions between wildfire occurrence and hydrometeorological conditions. Additionally, the resulting wildfire probability map, can aid in identifying high-risk areas, contributing to enhanced mitigation planning and prevention strategies.

Self-powered flexible wearable wireless sensing for outdoor work heatstroke prevention and health monitoring

Outdoor workers are susceptible to heatstroke in hot weather. Currently, there are no wearable devices on the market to assess the risk of heatstroke. This paper proposes a self-powered flexible wearable wireless sensing system for outdoor work heatstroke prevention and health monitoring (SWSM). SWSM consists of two parts, one part is a wearable heatstroke prevention patch based on flexible wireless sensing (WPFS) to monitor the wearer’s physiological indicators continuously. The other part is a self-powered device based on solar energy (SPDS), which can generate milliamp level current for powering the WPFS. With these indicators, the wearer’s activity category is confirmed by the multi-layer perceptron (MLP) model. WPFS contains a PVDF-HFD nanofibre cooling film (PNCF), flexible triboelectric nanogenerator (TENG)-based button (B-TENG), and functional sensing components. Temperatures under PNCF coverage can be up to 12% lower than environmental temperatures. B-TENG is used to adjust the mode. Based on the wearer’s physiological indicators a heatstroke risk assessment equation can be created to assess the potential risk of heatstroke. Finally, the data is displayed on the Internet of Things (IoT) platform. The system is wireless, self-powered, flexible wearable, and can be used for real-time prevention of heatstroke risk and health monitoring of outdoor workers.

Exceptional heatwaves and mortality in Europe: Greater impacts since the coronavirus disease 2019 outbreak

Record-breaking hot weather (exceptional heatwaves) has been increasingly common worldwide, posing a significant threat to human health. However, little is known about the effect of these exceptional heatwaves on mortality in Europe, especially since the coronavirus disease 2019 (COVID-19) outbreak, which converges with climate change to affect healthcare systems and human lives. We collected mortality data of 967 regions in 30 European countries over the last decade (2014–2023) from the Eurostat. A standard time-series analysis was used to estimate the effect of exceptional heatwaves by quasi-Poisson regression model, including the main effect (effect from heatwave intensity) and the added effect (effect from heatwave duration), on mortality for each region during two periods (before and since the COVID-19 outbreak). We used random effects meta-analysis to pool the mortality risk (i.e., relative risk [RR]) and burden (i.e., attributable fraction [AF]) associated with exceptional heatwaves, at the country level and for Europe as a whole. In Europe, the mortality burden attributable to main and added effects increased from 0.492% (95% CI: 0.488%–0.496%) to 1.276% (95% CI: 1.266%–1.285%) and from 0.307% (95% CI: 0.294%–0.318%) to 0.428% (95% CI: 0.407%–0.448%), respectively. Furthermore, substantial variations across countries were observed, with some countries such as France and Spain experiencing a large increase in the mortality burden attributable to exceptional heatwaves since the COVID-19 outbreak. Our findings underscore the urgent need for heat-health actions to consider the multi-effects of exceptional heatwaves amidst a warming climate.

DESIGN AND FABRICATION OF SMART ENVIRO GUARDIAN

This project tackles the challenge of maintaining a comfortable and healthy indoor environment in the face of unpredictable weather conditions. The primary objective is to develop an automated weather monitoring system integrated with a humidifier, leveraging Internet of Things (IoT) technology and a suite of sensors including the DHT11 for temperature and humidity, a rain sensor, an LDR sensor, and a BMP180 barometric pressure sensor. The system aims to monitor real-time weather data and adjust indoor conditions accordingly, enhancing comfort and reducing health risks such as respiratory issues, allergies, and mold growth. Additionally, it provides remote monitoring and real-time alerts through IoT connectivity, improving user convenience and energy efficiency. The methodology includes detailed requirement analysis, hardware and software development, testing, and deployment. This project promises to significantly improve indoor environmental quality, especially in regions with erratic weather patterns, by providing a robust, adaptable solution. Keywords: Automated Weather Monitoring, Climate Control, IoT Technology, Humidifier, DHT11 Sensor, Rain Sensor, LDR Sensor, Energy Efficiency

Models and observations agree on fewer and milder midlatitude cold extremes even over recent decades of rapid Arctic warming.

An apparent increase in observed cold extremes over recent decades in the northern midlatitudes has been reported, in contrast to robust decreases predicted by climate models. This discrepancy has led to suggestions that models fail to accurately simulate changes in weather patterns caused by Arctic warming. Here, we show that the observed frequency and intensity of midlatitude cold extremes have strongly decreased since 1990 and are consistent with modeled trends. The previously reported increase in cold extremes was overestimated due to an artifact of changing data coverage. We also show that the fraction of land with observed cold extreme increases over recent decades is consistent with model internal variability on top of a near-uniform forced reduction in cold extremes across the midlatitudes. Our results provide strong evidence of a decrease in midlatitude cold extremes over recent decades and consistency between models and observations.