Climate variability poses serious challenges to productivity and food safety in rain-fed semi-arid areas. A study on the impact of Tmax, Tmin, and precipitation on the yield of maize was performed in Ariyalur and Perambalur districts, Tamil Nadu, using historical data from 1985 to 2020 and future projection data from 2021 to 2100 under the Shared Socioeconomic Pathways-SSP2-4.5 climate change scenario. Climate extremes analysis shows the results that there is an increase in warm nights (TN90P), warm days (TX90p), heavy rainfall events (R10mm, R20mm), and shorter dry spells (CDD), reflecting more heat and extreme rainfall in both districts. Temperature is increasing considerably; Max and Min temperatures are projected to rise by 1.5 to 2°C by 2100. Patterns of precipitation are changing, with more frequent moderate rainfall events of 10-20 mm and fewer dry spells. From Ariyalur, in conditions of a rise in minimum temperature by 1°C, there has been a reduction of up to 38.2% in maize yield, and it explained 20-25% of variability in yield. Perambalur experiences a 21.7% yield reduction per 1°C with less intensity. The model from Ariyalur outperforms the one from Perambalur, adjusted R² being 0.967 and 0.511, respectively, which suggests that local sites have different sensitivities to climate. The findings from the present research signify the urgent need for adaptive strategies, including heat-tolerant varieties of maize, efficient irrigation, and integrated pest management, which could help mitigate climate risks.

Climate change profoundly affects agricultural insect pests by altering their biology, distribution, and interactions within agroecosystems, threatening global food security. Rising temperatures, elevated atmospheric CO₂, and shifting precipitation patterns accelerate pest development, expand geographic ranges, and increase voltinism, intensifying crop damage. These shifts disrupt traditional pest management frameworks, as phenological mismatches among pests, host plants, and natural enemies weaken biological control. Moreover, abiotic stresses compromise the performance of biocontrol agents, such as entomopathogenic fungi, necessitating climate-specific strain selection. Adaptive integrated pest management (IPM) strategies that incorporate real-time monitoring, predictive modeling, precision agriculture technologies, and emerging tools such as CRISPR and sterile insect techniques are essential for climate-resilient agriculture. Sustainable approaches that leverage natural products and minimize reliance on chemical pesticides further support ecosystem health. This review synthesizes current knowledge on climate-driven pest dynamics, range expansions, and tritrophic disruptions based on literature searched in Web of Science, Scopus, PubMed, and Google Scholar from January 2000 to November 2025 using Boolean strings. This review proposes a comprehensive climate-adaptive IPM framework to safeguard agricultural productivity amid ongoing environmental change.

Kerala, an ecologically sensitive state in southwestern India, is increasingly vulnerable to rainfall-induced disasters such as floods and landslides. This study analysed 124 years (1901–2024) of high-resolution daily rainfall data from the India Meteorological Department (IMD) to examine spatial and temporal trends across Kerala. The analysis assessed changes in rainy days and the frequency of heavy (HRF), very heavy (VHRF), and extremely heavy rainfall (EHRF) events, along with shifts in the onset of the southwest monsoon (SWM) and northeast monsoon (NEM) and rainfall irregularity using the Precipitation Concentration Index (PCI). Results revealed strong spatial heterogeneity: northern Kerala receives higher SWM rainfall (~3000 mm), while southern regions experience more intense rainfall during the NEM and winter seasons. Breakpoint analysis indicated a recent change in NEM rainfall around 2020, with a steep increase in slope from -0.527 to 23.048. High PCI values (11–21) in northern and central-western regions reflect strong rainfall concentration and elevated flood risks. Rainy days and EHRF events increased during the SWM and summer, while declines during the NEM and winter could affect water availability and winter cropping. Long-term projections suggest the SWM may advance toward May and the NEM extend into late October. These changing rainfall dynamics hold significant implications for agriculture, water management, and climate adaptation planning, emphasizing the need for location-specific strategies.

Accurate and usable potential evapotranspiration (PET) estimation is important for managing water resources around the world, planning agriculture, and adapting to climate change. Complex energy balance models yield valuable insights; yet practical applications necessitate straightforward, resilient, and simply implementable long-term monitoring methodologies. This study confirms a more straightforward empirical model that estimates monthly PET only utilizing MODIS land surface temperature (LST) data of 25 years (2000–2024), addressing a deficiency in the comprehension of simple model transferability across global climatic regimes. The LST products (MOD11A1/MYD11A1) processed in Google Earth Engine to confirm the accuracy of PET predictions against the FAO-56 Penman–Monteith (FAO-PM) technique, which was based on data from 58 ground-based meteorological stations in 5 Major Köppen–Geiger climate zones. The model was very accurate (R² = 0.76, RMSE = 30.02 mm/month); however, it was completely unique in different areas because of environmental controls. The model worked well in the Continental and Mediterranean climate zones (R² = 0.93, NSE = 0.88), but it had trouble in the Tropical Wet (R² = 0.39, NSE = -6.15) and Polar (R² = 0.64, NSE = -2.64) regions because of the moisture in the air and the complicated way energy is divided. The initial comprehensive analysis of basic LST-based model constraints sets essential standards for operational implementation and underscores the necessity for climate-zone-specific parameterization in this global, long-term validation. The results enhance the comprehension of environmental influences on remote sensing-derived PET estimation and inform water resource management in a dynamic climate.

Urban heat island (UHI) is a prevalent environmental hazard in modern cities, with higher surface and air temperatures than adjacent rural regions. The current study assessed the spatiotemporal distribution of land surface temperature (LST) in Iraq's Kurdistan region and the existence of urban heat islands during the daytime and at nighttime. The land surface temperature (LST) was composited from 2001 to 2024 using the historical Moderate-Resolution Imaging Spectroradiometer (MODIS) Terra satellite 8. The average LSTs of the rural and arid regions were contrasted with the average LSTs of the urban and suburban areas in three governorates of the study area, namely Erbil, Sulimaniyah, and Duhoke. Daytime and nighttime LST were also compared. The results revealed that the highest values of LST occurred in the urban region of the southern parts of the study area, where the mean value was 32.2 0C during the daytime. During the summer, Erbil had a higher temperature of 49.5 0C, while Sulimaniyah had the lowest (0.98 0C). According to annual data, almost 80% of the study region had an NLST score of 0.6 or 0.7. The biggest difference in LST mean value between urban and suburban regions was recorded in the summer daytime in Erbil city, with a value of 5.1 0C, while the smallest variances were reported in the fall season for all governorates in the study area, reaching 0.01 0C at night in Sulimaniyah city.

This study examines the trends and impacts of heat waves (HWs) and cold waves (CWs) in Eastern Uttar Pradesh, India, from 1961 to 2020, utilizing gridded daily maximum and minimum temperature data from the Indian Meteorological Department. This study analyzes the decadal totals of days, maximum continuous duration days, and mean maximum and minimum temperatures of HWs and CWs across nine meteorological stations. The findings reveal a significant increase in HW occurrences, particularly in stations like Fatehpur and Varanasi, while a decline in CW events is noted across the region. The Excess Heat Factor (EHF) index indicates a rising trend in heat stress events, and this study suggests that the intensity of HWs is increasing due to changes in temperature variability rather than mean warming alone.

Combining station observations with bias-corrected gridded climate data is crucial for reliable drought assessment in data-sparse regions. This study investigates the spatiotemporal characteristics of drought in Nineveh, Iraq, using the Standardized Precipitation Evapotranspiration Index at three- and six-month timescales (SPEI03 and SPEI06). Monthly station observations (1992-2013) were used to bias-correct TerraClimate data (2001-2023), which were then utilized to extend the record and compute SPEIs based on precipitation and potential evapotranspiration (PET). Drought frequency, duration, severity, and intensity were quantified, and trends were assessed using the Mann–Kendall test and Sen’s slope estimator. Results show notable interannual variability and a clear shift toward more frequent, severe, and persistent droughts in recent decades. The northern and northeastern areas emerged as drought hotspots, with Tel-Afar station experiencing the longest and most severe events. Comparisons between 2001–2011 and 2012–2023 reveal a marked intensification and expansion of severe and extreme drought zones. Trend analysis confirms widespread declines in moisture availability, especially for SPEI06, indicating increased exposure to prolonged water deficits. These findings highlight substantial spatial heterogeneity and emphasize the need for localized drought adaptation, improved water resource management, and early-warning systems to mitigate escalating risks to agriculture and livelihoods under a changing climate.