Impact Of Extreme Weather Events Research Articles

Extreme weather events and maternal health in low-income and middle-income countries: a scoping review

BackgroundDespite global efforts to improve maternal health and healthcare, women throughout the world endure poor health during pregnancy. Extreme weather events (EWE) disrupt infrastructure and access to medical services, however...

Powerless in the storm: Severe weather-driven power outages in New York State, 2017–2020

The vulnerability of the power grid to severe weather events is a critical issue as climate change is expected to increase extreme events, which can damage components of the power grid and/or lessen electrical power supply, resulting in power outages. However, largely due to an absence of granular spatiotemporal outage data, we lack a robust understanding of how severe weather-driven outages, their community impacts, and their durations distribute across space and socioeconomic vulnerability. Here, we pair hourly power outage data in electrical power operating localities (n = 1865) throughout NYS with urbanicity, CDC Social Vulnerability Index, and hourly weather (temperature, precipitation, wind speed, lightning strike, snowfall) data. We used these data to characterize the impact of extreme weather events on power outages from 2017–2020, while considering neighborhood vulnerability factors. Specifically, we assess (a) the lagged effect of severe weather on power outages, (b) common combinations of severe weather types contributing to outages, (c) the spatial distribution of the severe weather-driven outages, and (d) disparities in severe weather-driven outages by degree of community social vulnerability. We found that across NYS, 39.9% of all outages co-occurred with severe weather. However, certain regions, including eastern Queens, upper Manhattan and the Bronx of NYC, the Hudson Valley, and Adirondack regions were more burdened with severe weather-driven outages. Using targeted maximum likelihood estimation, we found that the frequency of heat-, precipitation-, and wind-driven outages disproportionately impacted vulnerable communities in NYC. When comparing durations of outages, we found that in rural regions, precipitation- and snow-driven outages lasted the longest in vulnerable communities. Under a shifting climate, anticipated increases in power outages will differentially burden communities due to regional heterogeneity in severe weather event severity, grid preparedness, and population socioeconomic profiles/vulnerabilities. As such, policymakers must consider these characteristics to inform equitable grid management and improvements.

Seasonal climate forecast-an important tool in managing the risk of extreme weather events in Australia's wheat industry

This study aims to investigate the benefits of using the Australian Community Climate and Earth-System Simulator-Seasonal Version 2 (ACCESS-S2), a state-of-the-art seasonal climate forecast (SCF) system, to improve contingent decision-making in the Australian wheat industry. Six locations in eastern Australia, three cultivars with varying maturities, and 17 times of sowing (TOS) were considered. Seasonal hindcasts from ACCESS-S2, initialized on the 1st May for the period 1981–2018 were linked with the Agricultural Production System sIMulator (APSIM)-Wheat model (v7.10) to (1) assess the hindcast skill of ACCESS-S2 in predicting the occurrence of extreme weather events and in predicting above/below long-term median wheat yield; (2) identify optimal management strategies in mitigating the impact of extreme weather events; and (3) quantify the yield benefit of using ACCESS-S2 at the key decision-making point of sowing against a baseline management situation (i.e., without the use of SCF). Research results showed that (1) ACCESS-S2 has varying skills in predicting extreme climate indices and wheat yield across the six locations at a seasonal lead time; (2) 68 % of cases had a yield gain using SCF information; (3) across all cases, there was an average yield gain of 281 kg/ha representing an increase of 13 %; (4) benefits of using SCF were seen across predicted wet years, neutral years and dry years accounting for 69 %, 65 % and 72 % respectively, and (5) the identified optimal management options varied from location to location with earlier sowing associated with either suntop or gregory or both being the optimal strategies at most of the locations considered. Overall, there is a demonstrated benefit in utilizing ACCESS-S2 forecasts in the Australian wheat industry to improve farm management decision-making. The benefit can occur in any climate state but with dry years being more likely and significant (with an average yield increase of 97 % across the locations).

Assessing Climate Change Impacts on Streamflow and Baseflow in the Karnali River Basin, Nepal: A CMIP6 Multi-Model Ensemble Approach Using SWAT and Web-Based Hydrograph Analysis Tool

Our study aims to understand how the hydrological cycle is affected by climate change in river basins. This study focused on the Karnali River Basin (KRB) to examine the impact of extreme weather events like floods and heat waves on water security and sustainable environmental management. Our research incorporates precipitation and temperature projections from ten Global Circulation Models (GCMs) under the Coupled Model Intercomparison Project Phase 6 (CMIP6). We applied thirteen statistical bias correction methods for precipitation and nine for temperatures to make future precipitation and temperature trend projections. The research study also utilized the Soil and Water Assessment Tool (SWAT) model at multi-sites to estimate future streamflow under the Shared Socioeconomic Pathway (SSP) scenarios of SSP245 and SSP585. Additionally, the Web-based Hydrograph Analysis Tool (WHAT) was used to distinguish between baseflow and streamflow. Our findings, based on the Multi-Model Ensemble (MME), indicate that precipitation will increase by 7.79–16.25% under SSP245 (9.43–27.47% under SSP585) and maximum temperatures will rise at rates of 0.018, 0.048, and 0.064 °C/yr under SSP245 (0.022, 0.066, and 0.119 °C/yr under SSP585). We also anticipate that minimum temperatures will increase at rates of 0.049, 0.08, and 0.97 °C/yr under SSP245 (0.057, 0.115, and 0.187 °C/yr under SSP585) for near, mid, and far future periods, respectively. Our research predicts an increase in river discharge in the KRB by 27.12% to 54.88% under SSP245 and 45.4% to 93.3% under SSP585 in different future periods. Our finding also showed that the expected minimum monthly baseflow in future periods will occur earlier than in the historical period. Our study emphasizes the need for sustainable and adaptive management strategies to address the effects of climate change on water security in the KRB. By providing detailed insights into future hydrological conditions, this research serves as a critical resource for policymakers and stakeholders, facilitating informed decision-making for the sustainable management of water resources in the face of climate change.

Impacts of Extreme Weather Events on Education Outcomes: A Review of Evidence

Abstract Extreme weather events are increasingly disrupting schooling. Yet, these are underrepresented in the climate change literature. Of 15 review articles on the economic impacts of climate change published since 2010, only three mention the impacts of climate change on education. We review available literature on the effects of weather extremes on education. We outline key pathways through which these events impact education outcomes, as well as the magnitude of those impacts. Evidence implies a significant and adverse relationship between heat and learning. Studies suggest surpassing a high temperature threshold makes learning difficult and results in learning losses. Across studies, each additional day subject to extreme heat reduces learning. Tropical cyclones, floods, and wildfires precipitate school closures, which halt learning. Evidence suggests that one day of school closures leads to one day of learning lost. Weather extremes also negatively impact education outcomes through health, nutrition, poverty, and fragility, among other distal pathways. We discuss the implications of this evidence for policy, including the need to adapt education systems to climate change. Mitigation and adaptation are both urgently needed as extreme weather events become more frequent and severe in the context of climate change.

Impact of extreme weather events on healthcare utilization and mortality in the United States.

Climate change is intensifying extreme weather events. Yet a systematic analysis of post-disaster healthcare utilization and outcomes for severe weather and climate disasters, as tracked by the US government, is lacking. Following exposure to 42 US billion-dollar weather disasters (severe storm, flood, flood/severe storm, tropical cyclone and winter storm) between 2011 and 2016, we used a difference-in-differences (DID) approach to quantify changes in the rates of emergency department (ED) visits, nonelective hospitalizations and mortality between fee-for-service Medicare beneficiaries in affected compared to matched control counties in post-disaster weeks 1, 1-2 and 3-6. Overall, disasters were associated with higher rates of ED utilization in affected counties in post-disaster week 1 (DID of 1.22% (95% CI, 0.20% to 2.25%; P < 0.020)) through week 2. Nonelective hospitalizations were unchanged. Mortality was higher in affected counties in week 1 (DID of 1.40% (95% CI, 0.08% to 2.74%; P = 0.037)) and persisted for 6 weeks. Counties with the greatest loss and damage experienced greater increases in ED and mortality rates compared to all affected counties. Thus, billion-dollar weather disasters are associated with excess ED visits and mortality in Medicare beneficiaries. Tracking these outcomes is important for adaptation that protects patients and communities, health system resilience and policy.

Dynamic volatility spillover relationships between the Chinese carbon and international energy markets from extreme climate shocks

The immense attention to climate change risk has stimulated increasing interest in carbon markets and their linkages to other markets. This study investigates the dynamic volatility spillover relationships between the Chinese carbon and international energy markets, and the impact of extreme weather events on the relationships. Empirical results show that the risk is transferred from the international gas (GAS) and crude oil (OIL) markets to the Chinese carbon market (GDC). The total volatility spillover among markets and the net spillover of GDC with other markets both have obvious periodicity. The shocks from extreme weather events and extremely high temperatures can amplify the trading volume of GDC, and strengthen the fluctuations of GDC, ultimately turning GDC from a risk receiver to a risk transmitter. The portfolio strategies based on the reversal phenomenon can achieve higher average returns compared to the Chinese and EU carbon markets. In addition, the shocks of the Sino-US trade war, the Covid-19 outbreak, the COP 26 and the Russo-Ukrainian war have different effects on the volatility spillovers among markets. Economic and policy implications for investors and regulators are also provided along with these findings.

The impact of extreme weather events exceeds those due to global-change drivers on coastal phytoplankton assemblages

Extreme wind and rainfall events have become more frequent phenomena, impacting coastal ecosystems by inducing increased mixing regimes in the upper mixed layers (UML) and reduced transparency (i.e. browning), hence affecting phytoplankton photosynthesis. In this study, five plankton assemblages from the South Atlantic Ocean, from a gradient of environmental variability and anthropogenic exposure, were subjected to simulated extreme weather events under a global change scenario (GCS) of increased temperature and nutrients and decreased pH, and compared to ambient conditions (Control). Using multiple linear regression (MLR) analysis we determined that evenness and the ratio of diatoms/ (flagellates + dinoflagellates) significantly explained the variations (81–91 %) of the photosynthesis efficiency (i.e. Pchla/ETRchla ratio) for each site under static conditions. Mixing speed and the optical depth (i.e. attenuation coefficient * depth, kdz), as single drivers, explained 40–76 % of the variability in the Pchla/ETRchla ratio, while GCS drivers <9 %. Overall, assemblages with high diversity and evenness were less vulnerable to extreme weather events under a GCS. Extreme weather events should be considered in global change studies and conservation/management plans as even at local/regional scales, they can exceed the predicted impacts of mean global climate change on coastal primary productivity.

Funding Resilience: Exploring International Climate Finance Strategies in African Contexts

This article addresses a gap in the existing literature by presenting comprehensive solutions to bolster international climate finance mechanisms, with a specific focus on alleviating the impact of extreme weather events in Africa. The core objective of this research revolves around examining innovative strategies that can augment the financial support flowing to African nations, ensuring their capacity to adeptly respond to the challenges presented by climate change. Through a critical analysis of the current landscape of international climate finance, along with the identification of successful models and a consideration of their limitations, the study aims to propose actionable recommendations. Beyond contributing to academic discourse, the article seeks to provide practical insights for policy-making, empowering African nations to enhance resilience and adapt to the escalating threats of extreme weather events amid a rapidly changing climate.

Effects of Extreme Weather Events on Nitrous Oxide Emissions from Rice-Wheat Rotation Croplands.

Cropland ecosystems are significant emission sources of N2O, but a limited number of studies have focused on the impact of extreme weather events on N2O fluxes from cropland. This present study integrated field observations and model simulations to explore the responses of N2O fluxes to extreme weather events in typical rice and wheat rotation croplands in the middle and lower reaches of the Yangtze River (MLRYR) in China. The findings revealed that the studied rice-wheat rotation cropland exhibited a net source of N2O over the three-year monitoring period, with annual cumulative N2O emissions ranging from 190.4 to 261.8 mg N m-2. N2O emissions during the rice and wheat growing seasons accounted for 29% and 71% of the total yearly emissions, respectively. Extreme heat events led to a 23% to 32% increase in observed N2O emissions from cropland. Observed N2O emissions from irrigated rice fields during extreme precipitation events were 45% lower than those during extreme drought events. In contrast, extreme precipitation events raised observed N2O emissions from rain-fed wheat fields by 36% compared to the multi-year average, while extreme drought events reduced N2O emissions from wheat fields by 20%. Regional simulations indicated that annual cumulative N2O emissions from croplands in the MLRYR are projected to increase from 207.8 mg N m-2 under current climate to 303.4 mg N m-2 in the future. Given the episodic nature and uncertainties associated with N2O emissions from cropland, further validation is necessary for utilizing the model to explore the effects of extreme weather events on N2O in cropland ecosystems.

Assessing the Risk of Extreme Storm Surges from Tropical Cyclones under Climate Change Using Bidirectional Attention-Based LSTM for Improved Prediction

Accurate prediction of storm surges is crucial for mitigating the impact of extreme weather events. This paper introduces the Bidirectional Attention-based Long Short-Term Memory (LSTM) Storm Surge Architecture, BALSSA, addressing limitations in traditional physical models. By leveraging machine learning techniques and extensive historical and real-time data, BALSSA significantly enhances prediction accuracy. Utilizing a bidirectional attention-based LSTM framework, it captures complex, non-linear relationships and long-term dependencies, improving the accuracy of storm surge predictions. The enhanced model, D-BALSSA, further amplifies predictive capability through a doubled bidirectional attention-based structure. Training and evaluation involve a comprehensive dataset from over 70 typhoon incidents in Macao between 2017 and 2022. The results showcase the outstanding performance of BALSSA, delivering highly accurate storm surge forecasts with a lead time of up to 72 h. Notably, the model exhibits a low Mean Absolute Error (MAE) of 0.0287 m and Root Mean Squared Error (RMSE) of 0.0357 m, crucial indicators measuring the accuracy of storm surge predictions in water level anomalies. These metrics comprehensively evaluate the model’s accuracy within the specified timeframe, enabling timely evacuation and early warnings for effective disaster mitigation. An adaptive system, integrating real-time alerts, tropical cyclone (TC) chaser, and prospective visualizations of meteorological and tidal measurements, enhances BALSSA’s capabilities for improved storm surge prediction. Positioned as a comprehensive tool for risk management, BALSSA supports decision makers, civil protection agencies, and governments involved in disaster preparedness and response. By leveraging advanced machine learning techniques and extensive data, BALSSA enables precise and timely predictions, empowering coastal communities to proactively prepare and respond to extreme weather events. This enhanced accuracy strengthens the resilience of coastal communities and protects lives and infrastructure from the escalating threats of climate change.

Impacts of extreme weather events on mortgage risks and their evolution under climate change: A case study on Florida

We develop an additive Cox proportional hazard model with time-varying covariates, including spatio-temporal characteristics of weather events, to study the impact of weather extremes (heavy rains and tropical cyclones) on the probability of mortgage default and prepayment. We compare the survival model with a flexible logistic model and an extreme gradient boosting algorithm. We estimate the models on a portfolio of mortgages in Florida, consisting of 69,046 loans and 3,707,831 loan-month observations with localization data at the five-digit ZIP code level. We find a statistically significant and non-linear impact of tropical cyclone intensity on default as well as a significant impact of heavy rains in areas with large exposure to flood risks. These findings confirm existing results in the literature and also provide estimates of the impact of the extreme event characteristics on mortgage risk, e.g. the impact of tropical cyclones on default more than doubles in magnitude when moving from a hurricane of category two to a hurricane of category three or more. We build on the identified effect of exposure to flood risk (in interaction with heavy rainfall) on mortgage default to perform a scenario analysis of the future impacts of climate change using the First Street flood model, which provides projections of exposure to floods in 2050 under RCP 4.5. We find a systematic increase in risk under climate change that can vary based on the scenario of extreme events considered. Climate-adjusted credit risk allows risk managers to better evaluate the impact of climate-related risks on mortgage portfolios.

Analysis of Connected Vehicle Data to Quantify National Mobility Impacts of Winter Storms for Decision Makers and Media Reports

Traditional techniques of monitoring roadway mobility during winter weather have relied on embedded road sensors, roadside cameras, radio reports from public safety staff, or public incident reports. However, widely available connected vehicle (CV) data provides government agencies and media with a unique opportunity to monitor the mobility impact of inclement weather events in near real-time. This study presents such a use case that analyzed over 500 billion CV records characterizing the spatial and temporal impact of a winter storm that moved across the country from 21 to 26 December 2022. The analysis covered 97,000 directional miles of interstate roadway and processed over 503 billion CV records. At the storm’s peak on 22 December at 5:26 PM Eastern Time, nearly 4800 directional miles of interstate roadway were operating under 45 mph, a widely accepted indicator of degraded interstate conditions. The study presents a methodological approach to systematically assess the mobility impact of this winter event on interstate roadways at a national and regional level. The paper then looks at a case study on Interstate 70, a 4350 directional mile route passing through ten states. Statewide comparison showed Ohio was most impacted, with 9% of mile-hours operating below 45 mph on 23 December. High-Resolution Rapid Refresh weather data provided by the National Oceanic and Atmospheric Administration was integrated into the analysis to provide a visualization of the storm’s temporal path and severity. We believe the proposed metrics and visualizations are effective tools for communicating the severity and geographic impact of extreme weather events to broad non-technical audiences.

Exploring the relationship between extreme weather events, urbanization, and food insecurity: Institutional quality perspective

This study examines the potential for institutional quality to address Africa's trilemma of food insecurity, rapid urbanization, and extreme weather events.Using a two-stage least squares regression analysis of 28 African countries between 2012Q1 and 2021Q4, our results suggest that extreme weather events have a significant negative impact on food security, particularly in urban areas.We found that institutional quality can have a significant positive impact on food security and can mitigate the negative effects of extreme weather events on food security. Our study also decomposed the food security index into its four components and found that extreme weather events negatively affect the affordability, availability, quality, and natural resource resilience of food. The findings are robust to the use of the feasible generalized least squares and panel-corrected standard errors estimation techniques.The policy implications of our study are significant, as our findings suggest that central governments can take practical steps to minimize the impact of extreme weather events on food security. Specifically, investing in disaster preparedness, improving infrastructure, and promoting agricultural development can all help improve food security. Additionally, improving institutional quality can be an effective way to mitigate the negative impact of extreme weather events on food security.

Extensive analysis of rain in australia by exploratory data analysis, feature engineering and modeling

Accurate rainfall forecasts help in planning outdoor activities, agricultural practices, and water resource management, thereby mitigating the impact of extreme weather events. This article provides an in-depth analysis of rainfall in Australia, focusing on predicting whether it will rain tomorrow using logistic regression. The research aims to develop an accurate model to help predict rainfall events for better preparedness and planning. We obtained datasets from a number of Australian weather stations. The dataset contains 142,193 daily weather observations spanning approximately ten years. The recorded information includes various details such as date, location, humidity, wind direction, clouds, temperature, etc. This shows that the model performs well in distinguishing between rainy and non-rainy days with an accuracy of about 0.875. The findings of this study have important implications for various stakeholders including meteorologists, disaster management agencies, and the public.

Seasonal differences in continental outflows and marine emissions of organic aerosols in the East China Sea: Insight from the variation of saccharides

Water-soluble organic species play an important role in cloud condensation nuclei and cloud formation in marine atmosphere. The present study investigated the influence of continental outflows and marine biogenic emission on organic aerosol in the East China Sea from the perspective of saccharides during a cold season and a warm season in 2019, respectively. Driven by the East Asian continental outflows during the cold season, levoglucosan emitted from biomass burning were the dominant species and were mostly concentrated in fine mode (<2.5 μm). Biogenic species, including primary sugars and sugar alcohols, were the dominant species in warm season, accounting for a high fraction of WSOC with 5.48 ± 3.29%, and were mostly distributed in coarse mode (2.5–10 μm). This trend was consistent with sea salt components (Na+ and Cl−) when air masses originated from oceanic areas, suggesting that marine emissions could be the major sources of saccharides during the warm season. Particularly, a dramatic increase in biogenic saccharides (glucose, sucrose, mannitol and arabitol) was observed when experienced the “Super Typhoon Lekima”. Concentrations of these species were 2 to 5 times higher than that in normal days, illustrating the significant impact of extreme weather events on the emissions of biogenic aerosols in marine area. This study improves the understanding of combined impacts of continental outflows and marine emissions on water-soluble organic species in the East China Sea, again highlighting the important contribution of marine biogenic emissions under extreme weather conditions.

Impacts of extreme weather events on microplastic distribution in coastal environments

Microplastic pollution is a pressing environmental threat to the ecosystem, which can be influenced by varying weather factors. With arising weather extremes in recent years, it is crucial to assess the weather effects on coastal microplastic pollution. In this study, we conducted a year-long baseline survey on beach sediment and surface seawater in Hong Kong, and additional surveys after rainstorm and typhoon events. Our data reveals that microplastic abundance was 5 times higher in wet season over dry season. Yet, the seasonal variation was insignificant when considering only the baseline condition, suggesting the role of extreme weather as the main driver of the seasonal variation in microplastic distribution. Typhoons and rainstorms induced 5.1 to 36.4 times and 1.9 to 11.7 times more microplastics in beach sediment, respectively, and 3.5 to 6.0 times and 2.5 to 4.3 times more microplastics in surface seawater, respectively. The larger microplastic mass in beach sediment and the increased proportion of hard fragments under extreme weather conditions indicate the larger mobility of heavier plastic debris from a wider source. We identified positive correlations between plastic levels and multiple weather factors (including rainfall, wind and tide), suggesting the potential terrestrial inputs of microplastics via surface runoff and wind transport, and the potential redistribution of microplastics from deep to surface sediment via wave agitation. We also identified a strongly positive correlation between macro- and microplastic abundance in beach sediment, suggesting the potential plastic fragmentation under strong wave abrasion, which may intensify the coastal microplastic pollution. This study sheds light on the need for reinforced security of waste management systems to avoid terrestrial plastic inputs under extreme weather forces, as well as the timing of coastal cleanup work in terms of limiting plastic fragmentation and achieving better cleanup efficiency.

Summer heatwaves, wind production and electricity demand in Southern Europe: climatic conditions and impacts

Electricity demand for cooling and heating is directly related to weather and climate, primarily through ambient temperature. In Southern Europe, the maximum electricity demand for cooling in summer can be more pronounced than in winter, especially during heat wave (HW) episodes. With the growth of renewable technologies in the energy mix, the dependency of the electricity system on the weather is becoming evident not just from the demand side, but also from the energy supply side. From the resources point of view, summer wind presents a minimum on its annual cycle, so a combination of maximum electricity demand can coincide with a minimum of wind power production. This study presents a strong multidisciplinary focus, merging climate, energy and environmental discipline, due to their relevant connections in Southern Europe where important climate change stresses are expected. The combined anomalies of electricity demand and wind production during heat wave episodes are quantified at the country level, taking into account the HW extension. The summer period (1989-2019) of ERA5 reanalysis and E-OBS-21.0e data is used for atmospheric magnitudes and the Copernicus climate change service (C3S) energy dataset for demand. In heat wave events, an increase of 3.5%–10.6% in electricity demand and a decrease up to −30.8% in wind power production is obtained, with variability depending on the country. The greater the extension of the HW, the greater the anomalies. Different weather regimes related to heatwaves also play a role on this range of values. Therefore, the impact of extreme weather events, such as heatwaves, on wind power production in conditions of high electricity demand, should be considered in the energy supply strategy and planning in order to minimize the impact of these events on an electricity system with high penetration of renewables.

Cold Season Rain Event Has Impact on Greenland's Firn Layer Comparable to Entire Summer Melt Season

AbstractRainfall high on the Greenland Ice Sheet is an emerging phenomenon with consequences for the thermal and structural makeup of the surface layer. We document changes to Greenland's firn column due to a 4‐day cold‐season warm/rain event. Heavy precipitation occurred with a sudden 30°C increase in air temperature, reaching 0°C at 2,000 m elevation. Thermistor strings within the firn layer across a 35 km transect show rapid warming of 6°–23°C reaching depths of 2–10 m. Antecedent conditions governed the magnitude, duration, and depth‐distribution of sensible and latent heat added to the firn column. Heat fluxes from the firn layer required up to 8 weeks to recover to baseline, a significant fraction of the winter period. The amount of liquid water refrozen in the firn column was ∼20%–100% of the prior summer demonstrating the impact of extreme weather events on the ice sheet's evolution and runoff characteristics.

EFFECTS OF GLOBAL CLIMATE CHANGE: ADAPTING AGRICULTURAL CROPS FOR A WARMER WORLD

With changing weather patterns, increased CO2 levels, and more frequent extreme weather events, the need to adapt crops to a warmer world is paramount. This study aims to provide a comprehensive overview of the challenges and potential solutions in adapting crops to climate change. The approach taken in this review paper involves examining the greenhouse effect, the causes of climate change, and the evidence supporting its existence. We also explore how changing weather patterns affect crop growth and the physiological effects of increased CO2 on plants. Additionally, the impact of extreme weather events on agriculture is discussed. This study reveals the importance of developing heat-resistant crop varieties and implementing climate-smart agricultural practices. By embracing genetic modifications, innovative farming practices, and technology in agriculture, we can enhance agricultural resilience and mitigate the adverse effects of climate change on crop production. Adapting crops for a warmer world is crucial for ensuring food security and sustainable agriculture. The findings of this study emphasize the need for continued research, innovation, and policy interventions to address the challenges posed by climate change. The results underscore the importance of building resilient agricultural systems and promoting sustainable practices for the well-being of present and future generations.