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In recent years, the water level of the Dongting Lake (DTL) has been continuously low, and the wetland area and landscape pattern have changed significantly. Considering the obvious spatial heterogeneity of water regime changes in different waters of the DTL, this paper takes two core areas of the South Dongting Lake Nature Reserve (SDLNR) as study areas and analyzes the spatial distribution characteristics of the wetland blue–green landscape patterns by using remote sensing image data and hydrological and meteorological data. The multi-scale correlation between runoff, precipitation, temperature, and evapotranspiration in the SDLNR was studied via cross-wavelet transform analysis. The results show the following: (1) The change in the blue–green spatial patterns in different regions in different periods is inconsistent, and this inconsistency is related to the topography, climate, and human activities in each region; (2) there are seasonal fluctuations in precipitation, air temperature, and evapotranspiration in the SDLNR. Among them, the annual mean temperature shows a rising trend and passes the significance test with 95% confidence, while the annual mean precipitation and annual mean evapotranspiration show no significant change trend; and (3) our Pearson correlation analysis and cross-wavelet change results show that precipitation and temperature are strongly correlated with runoff, with a resonance period of 8–16 months, while the correlation between evapotranspiration and runoff is not significant. We recommend that policymakers establish an effective early warning system and make plans to store water through micro-terrain transformation in possible climate change treatments and strategies. [ABSTRACT FROM AUTHOR]

The high variability of the sub-desert climate, especially in the south-eastern region of Morocco, has severe socioeconomic impacts on the inhabitant's way of life, as is the case in the Daoura watershed, where this variability is associated with droughts or exceptional rainfall. The data collected from the Guir-Ziz-Rheriss hydraulic agency were processed, corrected and analyzed using the Climatol package (version 4.0.0) developed in R software to homogenize rainfall data. Through this work, the significance and amplitudes of annual and monthly rainfall trends were defined using the Mann-Kendall test and Sens's slope estimator, while comparing the results of raw and homogenized data. The Daoura watershed has a sub-desert climate, and the homogenization process revealed a few rainfall stations with significant positive trends at confidence levels ranging from 90% to 95%. According to the raw and homogenized data, the majority of these stations are located in the High Atlas (CR1) and Anti-Atlasic (CR2) zones, where there is considerable spatiotemporal variability in rainfall from one year to the next. The essential objective of this scientific paper was to analyze the spatiotemporal variability of monthly and annual precipitation and to study their trends through rainfall data homogenized by the climatol package (version 4.0.0) from 13 stations over a period (1957-2018). The contribution of this study to science is the rainfall data it offers, which is useful for managing natural resources in sub-desert areas. [ABSTRACT FROM AUTHOR]

Global climate change is considered one of the greatest environmental threats in the world. It is expected to significantly change the global hydrological cycle. The two main water cycle components, potential evapotranspiration (PET) and precipitation (P), are closely related to vegetation dynamics. In this study, the partial correlation analysis method was used to analyzed the relationship between Normalized Difference Vegetation Index (NDVI) and climate factors (PET and P) based on grid cells. PET was calculated by FAO-56 Penman-Monteith method. Moreover, we also investigated the NDVI and climate factors in different vegetation cover types. The results showed that grassland, forest and cropland in China were positively correlated with PET and P. The time scales of the maximum partial correlation coefficients between NDVI and PET of grassland vegetation were mostly longer than 5-6 months. These time scales were longer than the time scales related to P. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China, whereas the spatial distribution of related time scales was the opposite. The partial correlation coefficients between NDVI and PET, P of forest vegetation were higher in northern China. However, the spatial distribution of related time scales was the opposite. The correlations between NDVI and PET, P of cropland vegetation and the time scales related to PET had clear spatial heterogeneity. The time scale of the correlation between NDVI and P for cropland in the northern China was about 2 months. P had a strong influence on the growth of various types of vegetation in the study area, and grassland vegetation was affected by P over the shortest time scale. We compare and analyze the results of this study with other related studies. These results provide a reference for exploring the dynamic changes in different vegetation types and factors impacting them. [ABSTRACT FROM AUTHOR]

Analyzing long-term climate changes is a prerequisite for identifying hotspot areas and developing site-specific adaptation measures. The current study focuses on assessing changes in precipitation, maximum and minimum temperatures, and potential evapotranspiration in Zambia and Malawi from 1981 to 2021. High-resolution precipitation and temperature datasets are used, namely, Climate Hazards Group InfraRed Precipitation with Station data (0.05°) and Multi-Source Weather (0.1°). The Mann–Kendall trend test and Sen's Slope methods are employed to assess the changes. The trend analysis shows a non-significant increase in annual precipitation in many parts of Zambia and Central Malawi. In Zambia and Malawi, the average annual and seasonal maximum and minimum temperatures show a statistically significant increasing trend (up to 0.6 °C/decade). The change in precipitation during the major rainy seasons (December–April) shows a non-significant increasing trend (up to 3 mm/year) in a large part of Zambia and Central Malawi. However, Malawi and Northern Zambia show a non-significant decreasing trend (up to −5 mm/year). The change in December–April precipitation significantly correlates with El Niño–Southern Oscillation (Indian Ocean Dipole) in Southern (Northern) Zambia and Malawi. To minimize the impact of the observed changes, it is imperative to develop adaptation measures to foster sustainability in the region. [ABSTRACT FROM AUTHOR]

Agricultural production across the African continent is subjected to various effects of climate variability. One of the main staple foods in Sub-Saharan Africa is maize. However, limited scientific research has recently focused on understanding the possible effects of hydro-climatic variability on maize production. The aim of the present work was to contribute to policy and climate adaptation, thus reducing the vulnerability of maize production to climate change over Equatorial Africa. This study firstly examined long-term trends of precipitation (PRE), soil moisture (SM), actual evapotranspiration (E), and potential evapotranspiration (Ep), as well as surface air temperatures, including the minimum (TMIN) and maximum (TMAX). Secondly, the relationship between maize production and these climate variables was quantified for 18 Equatorial African countries (EQCs) over 1980−2021. To assess the linear trends, Mann–Kendall and Sen's slope tests were used to quantify the magnitude of the hydro-climatic variable trends at the 5% significance level, and Pearson's correlation coefficient was used to evaluate the relation of these climate parameters with the maize production. The annual mean PRE declined at 0.03 mm day−110a−1. Other climate variables increased at different rates: SM at 0.02 mmday−110a−1, E at 0.03 mm day−110a−1, Ep at 0.02 mm day−1 10a−1, TMIN and TMAX at 0.01 °C day−110a−1. A regional analysis revealed heterogeneous significant wet–dry and warm–cool trends over the EQCs. While, spatially, dry and warm climates were observed in the central to eastern areas, wet and warm conditions dominated the western regions. Generally, the correlations of maize production with the E, Ep, TMAX, and TMIN were strong (r > 0.7) and positive, while moderate (r > 0.45) correlations of maize production with PRE and SM were obvious. These country-wide analyses highlight the significance of climate change policies and offer a scientific basis for designing tailored adaptation strategies in rainfed agricultural regions. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change has already affected drought severity and risk across many regions, and climate models project additional increases in drought risk with future warming. Historically, droughts are typically caused by periods of below‐normal precipitation and terminated by average or above‐normal precipitation. In many regions, however, soil moisture is projected to decrease primarily through warming‐driven increases in evaporative demand, potentially affecting the ability of negative precipitation anomalies to cause drought and positive precipitation anomalies to terminate drought. Here, we use climate model simulations from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate how different levels of warming (1, 2, and 3°C) affect the influence of precipitation on soil moisture drought in the Mediterranean and Western North America regions. We demonstrate that the same monthly precipitation deficits (25th percentile relative to a preindustrial baseline) at a global warming level of 2°C increase the probability of both surface and rootzone soil moisture drought by 29% in the Mediterranean and 32% and 6% in Western North America compared to the preindustrial baseline. Furthermore, the probability of a dry (25th percentile relative to a preindustrial baseline) surface soil moisture month given a high (75th percentile relative to a preindustrial baseline) precipitation month is 6 (Mediterranean) and 3 (Western North America) times more likely in a 2°C world compared to the preindustrial baseline. For these regions, warming will likely increase the risk of soil moisture drought during low precipitation periods while simultaneously reducing the efficacy of high precipitation periods to terminate droughts. Plain Language Summary: Regional warming associated with climate change is already making droughts worse in many places. This trend is expected to continue as global temperatures increase in response to continued emissions of greenhouse gases. With increasing temperatures, soils are projected to lose water to the atmosphere as it draws more and more moisture from the land surface, increasing drought risk. At the same time, climate change is also projected to shift precipitation patterns. This means it is important to understand how changes in rainfall will cause and end droughts in the future. Using state‐of‐the‐art climate models, we investigate how future warming will impact the influence of precipitation on soil moisture in the Mediterranean and Western North America. Our results suggest that current levels of monthly precipitation will be insufficient to alleviate drought conditions in a warmer world. Key Points: Warming causes declines in soil moisture in the Mediterranean and Western North AmericaThe probability of a moderate soil moisture drought increases during both low and high precipitation anomalies, even at 1°C of warmingThe ability of large positive precipitation anomalies to terminate soil moisture droughts will be substantially reduced in a warmer world [ABSTRACT FROM AUTHOR]

Understanding runoff dynamics is vital for effective water management in climate-affected areas. This study focuses on the Dayekou basin in China's Qilian Mountains, known for their high climate variability. Using 25 years of data (1994–2018) on river runoff, precipitation, and temperature, statistical methods were applied to explore the annual variations and climate change impacts on these parameters. Results reveal a significant variability in the river runoff (132.27 to 225.03 mm), precipitation (340.19 to 433.29 mm), and average temperature (1.38 to 2.08 °C) over the period. Decadal rising rates average 17 mm for runoff, 17 mm for precipitation, and 0.25 °C for temperature, with the peak precipitation and runoff occurring in 1998–2000, 2008, and 2016. The annual runoff distribution also exhibited a unimodal pattern, peaking at 39.68 mm in July. The cumulative runoff during low periods constituted only 13.84% of the annual total, concentrated in the second half of the year, particularly during the June-October flood season. The correlation analysis underscored a strong relationship between river runoff and precipitation (correlation coefficient > 0.80), while the temperature correlation was weaker (correlation coefficient < 0.80). This 25-year analysis provides valuable insights into runoff variation, elucidating the interconnected effects of temperature and precipitation in the Dayekou basin, with substantial implications for sustainable development amid climate challenges. [ABSTRACT FROM AUTHOR]

This study analyzes daily temperature and precipitation data collected from 44 weather stations throughout New York, New Jersey, and Connecticut to assess and quantify the historical climatic changes within these states. The study conducts a detailed examination of spatial and temporal trends, focusing on specific stations that best represent the climatic diversity of each area. A critical analysis aspect involves comparing temperature trends in urban and suburban areas, mainly focusing on New York City. The findings reveal a significant upward increasing trend in average temperatures across all seasons, with urban areas, especially NYC, exhibiting the most marked increases. This trend is notably sharp in the spring, reflecting climate change's escalating influence. The study also observes an increase in the annual average temperatures and a concurrent decrease in the variability of temperature ranges, suggesting a stabilization of temperature fluctuations over time. Also, we identified a notable increase in heat wave frequency, more so in urban locales than in their suburban counterparts. Analysis of precipitation patterns, particularly in NYC, reveals a decline in snowfall days, consistent with the general warming trend. The results demonstrate significant trends in seasonal average temperatures, a decrease in the variability of temperatures, and a rise in heat wave occurrences, with urban areas typically experiencing warmer conditions. This comprehensive study highlights the need for a more in-depth analysis of spatial precipitation trends. It underscores the importance of continued research in understanding the multifaceted impacts of climate change, particularly in differentiating urban and rural experiences. [ABSTRACT FROM AUTHOR]

The influence of climate feedbacks on regional hydrological changes under warming is poorly understood. Here, a moist energy balance model (MEBM) with a Hadley Cell parameterization is used to isolate the influence of climate feedbacks on changes in zonal‐mean precipitation‐minus‐evaporation (P − E) under greenhouse‐gas forcing. It is shown that cloud feedbacks act to narrow bands of tropical P − E and increase P − E in the deep tropics. The surface‐albedo feedback shifts the location of maximum tropical P − E and increases P − E in the polar regions. The intermodel spread in the P − E changes associated with feedbacks arises mainly from cloud feedbacks, with the lapse‐rate and surface‐albedo feedbacks playing important roles in the polar regions. The P − E change associated with cloud feedback locking in the MEBM is similar to that of a climate model with inactive cloud feedbacks. This work highlights the unique role that climate feedbacks play in causing deviations from the "wet‐gets‐wetter, dry‐gets‐drier" paradigm. Plain Language Summary: Climate feedbacks, which act to amplify or dampen global warming, play an important role in shaping how the climate system responds to changes in greenhouse‐gas concentrations. Here, we use an idealized climate model, which makes a simplified assumption about how energy is transported in the atmosphere, to examine how climate feedbacks influence the patterns of precipitation and evaporation change under global warming. We find that the cloud feedback acts to narrow the band of rainfall on the equator known as the Intertropical Convergence Zone and that the surface‐albedo feedback acts to shift the location of maximum rainfall. We also find that the cloud feedback accounts for most of the uncertainty associated with feedbacks in regional hydrological change under warming. The idealized model with a locked cloud feedback also simulates a change in precipitation and evaporation that is similar to a comprehensive climate model with an inactive cloud feedback. Key Points: A moist energy balance model (MEBM) is used to investigate the influence of climate feedbacks on regional hydrological changes under warmingCloud feedbacks act to narrow and increase tropical P − E and are the dominant source of feedback uncertainty in regional hydrological changesThe MEBM with a locked cloud feedback largely replicates a climate model with an inactive cloud feedback [ABSTRACT FROM AUTHOR]

Agro-climatic indicators (AgcIs) provide a suitable tool to evaluate the implications of climate change on agriculture by simplifying plant–climate interactions. However, developing reliable AgcIs requires high-quality historical climate datasets. Consequently, reanalysis products (RPs) are frequently used as a potential reference dataset for observed climate in agricultural studies. This study aims to compare five RPs (ERA5, ERA5-Land, SCOPE Climate, FYRE Climate, and RFHR) at reproducing observed AgcIs over France. The RPs are evaluated against the SYNOP meteorological data over the 1996–2012 period, focusing on six AgcIs specific to apple, maize, and vine crops. The findings show that RPs perform well in reproducing temperature-based AgcIs, with some slight discrepancies in areas with complex topography. However, all RPs tend to overestimate precipitation amounts and to underestimate dry days, leading to a poor performance in reproducing precipitation-based AgcIs. This study emphasizes the need for a thorough evaluation of the RPs in developing both temperature-based and precipitation-based AgcIs, especially if findings are intended to support operational agricultural decision-making. [ABSTRACT FROM AUTHOR]

Water is a valuable yet scarce resource in agriculture. Optimizing crop production relies on irrigation, but within the framework of sustainable agriculture, efforts should prioritize reducing irrigation water usage. Unfortunately, climate change increases plant water requirements, consequently heightening the need for irrigation. The aim of the research was to estimate the water needs of asparagus during the fern growth period (21 June–31 August) using the Blaney–Criddle method. The study also aimed to determine trends in changing water needs and estimate precipitation deficits. Calculations were carried out for the years 1981–2020 across four provinces in central Poland. The research indicated that water needs varied between 233 mm and 242 mm, depending on the province. Over the forty-year period, all provinces displayed an increasing trend in water needs, with a rise of 3.1 mm to 6.2 mm per decade. Between 21 June and 31 August, rainfall deficits occurred with varying intensity in all provinces during normal, medium dry and very dry years. The values of water needs and rainfall deficit, calculated as a result of the research, are of key importance in the design, construction, and management of drip irrigation systems for asparagus plantations in central Poland. [ABSTRACT FROM AUTHOR]

Microbial-driven processes, including nitrification and denitrification closely related to soil nitrous oxide (N2O) production, are orchestrated by a network of enzymes and genes such as amoA genes from ammonia-oxidizing bacteria (AOB) and archaea (AOA), narG (nitrate reductase), nirS and nirK (nitrite reductase), and nosZ (N2O reductase). However, how climatic factors and agricultural practices could influence these genes and processes and, consequently, soil N2O emissions remain unclear. In this comprehensive review, we quantitatively assessed the effects of these factors on nitrogen processes and soil N2O emissions using mega-analysis (i.e., meta-meta-analysis). The results showed that global warming increased soil nitrification and denitrification rates, leading to an overall increase in soil N2O emissions by 159.7%. Elevated CO2 stimulated both nirK and nirS with a substantial increase in soil N2O emission by 40.6%. Nitrogen fertilization amplified NH4+-N and NO3−-N contents, promoting AOB, nirS, and nirK, and caused a 153.2% increase in soil N2O emission. The application of biochar enhanced AOA, nirS, and nosZ, ultimately reducing soil N2O emission by 15.8%. Exposure to microplastics mostly stimulated the denitrification process and increased soil N2O emissions by 140.4%. These findings provide valuable insights into the mechanistic underpinnings of nitrogen processes and the microbial regulation of soil N2O emissions. [ABSTRACT FROM AUTHOR]

This paper matched the data for all non-financial listed firms in China from 1990 to 2017 with weather data and used temperature and precipitation to represent climate change. Using daily temperature and daily precipitation data, we constructed temperature bins and precipitation bins. This study presents a potential first empirical analysis that applies the risk perception theory to comprehensively examine the impacts of daily temperature and daily precipitation on firms' debt financing costs. Compared to a day with a suitable temperature, a day with temperatures above 30 °C increases firms' debt financing costs by 0.2411%. Compared to a sunny day, a day with torrential rain increases firms' financing costs by 0.2697%. Approximately $122.13 million and $20.49 million in extra costs were incurred across all firms in 2017. We conducted a series of heterogeneity tests. We found that the negative impacts of climate change on debt financing costs are greater for firms that investor considers more vulnerable. Finally, we found two possible mechanisms. First, from the firm's subjective initiative perspective, firms that respond proactively during seasons of high temperatures and heavy rains can reduce the negative impacts. Second, adverse weather caused by climate change may also increase firms' debt financing costs by blocking travel. These results enrich the literature on firms' debt financing costs and contribute to the understanding of climate change's impact on firms. [Display omitted] • An extra high-temperature day increases firms' debt financing cost by 0.2411%. • An extra heavy-rain day increases firms' debt financing costs by 0.2697%. • The negative impacts will be greater for those firms that investor considers more vulnerable. • Firms' response proactively can reduce the negative impacts. • Climate change also may increase firms' debt financing costs by blocking travel. [ABSTRACT FROM AUTHOR]

Coffee is one of the most traded crops worldwide. In the state of Veracruz, Mexico, coffee has been a strategic crop due to its economic, social, environmental, and cultural characteristics that differentiate it from other crops, contributing to the economy of almost 86,000 producers. Several studies have shown that climate is the main cause of the decrease in coffee production and yield. Due to the multi-faceted importance of coffee for the state, the relationship of coffee production and yield with the variables of precipitation (mm) and temperature (°C) from 2003 to 2022 was analyzed through the implementation of a mathematical model that was able to identify that both the total volume of coffee production is decreasing on average at a rate of 7614.9 Mg year−1 as well as the yield, with a significant decrease of 0.106 Mg year−1. It was also found that the optimum temperature value is 18.7 °C, and the optimal precipitation is 1700 mm for the development of coffee. This model also shows that yield is more sensitive to temperature than to precipitation in the study area. Through the application of surveys to 360 producers in 16 coffee-growing municipalities, seven stressors were identified that together hinder the continuity of the coffee industry in the state. These stressors are (1) economic, (2) climatic, (3) land use, (4) technical, (5) social, (6) political, and (7) other. Finally, some strategies are herein proposed to improve coffee production towards greater sustainability, such as agricultural restructuring at the national, regional, and local levels as well as programs and policies to support producers for the continuity of the crop in the region. [ABSTRACT FROM AUTHOR]

Extreme precipitation events are occurring more intensely in Canada. This can contaminate water sources with enteric pathogens, potentially increasing the risk of acute gastrointestinal illness. This study aimed to investigate the relationship between extreme precipitation and emergency department (ED) visits for acute gastrointestinal illness in Toronto from 2012 to 2022. Distributed lag non-linear models were constructed on ED visit counts with a Quasi Poisson distribution. Extreme precipitation was modelled as a 21-day lag variable, with a linear relationship assumed at levels ≧95th percentile. Separate models were also conducted on season-specific data sets. Daily precipitation and gastrointestinal illness ED visits ranged between 0 to 126 mm, and 12 to 180 visits respectively. Overall, a 10-mm increase in precipitation >95th percentile had no significant relationship with the risk of ED visits. However, stratification by seasons revealed significant relationships during spring (lags 1–19, peak at lag 14 RR = 1.04; 95% CI: 1.03, 1.06); the overall cumulative effect across the 21-day lag was also significant (RR = 1.94; 95% CI: 1.47, 2.57). Extreme precipitation has a seasonal effect on gastrointestinal health outcomes in Toronto city, suggesting varying levels of enteric pathogen exposures through drinking water or other environmental pathway during different seasons. [ABSTRACT FROM AUTHOR]

The process of climate warming significantly affects agroclimatic resources and agricultural production. We study the agroclimatic resources and their variability on the territory of the Republic of Bashkortostan (Russia). The Bashkortostan has a high agricultural potential and holds a leading position in the country in the production of grain crops, potatoes, milk, and honey. Currently, no detailed studies have been conducted for this area to assess the effects of global climate change on agro-climatic resources. World experience shows such research becomes strategically important for regions with powerful agricultural production. We used the sums of average daily air temperatures above 0 and 10 °C, the G.T. Selyaninov hydrothermal coefficient, and the Ped aridity (humidification) index as agroclimatic indicators. We used data of long-term meteorological observations of 30 meteorological stations for the period of 1961–2020. We revealed the long-term dynamics of the agroclimatic indicators and the spatial and temporal regularities in their distribution on the territory of Bashkortostan. There is a steady increase in the sums of average daily air temperatures above 0 and 10 °C. Against this background, aridity increases, which is especially manifested in the southern parts of the Republic of Bashkortostan. We assessed the impact of agroclimatic indicators on the main types of agricultural crops in the republic. We revealed that the greatest positive impact on the yield of oilseeds, cereals, and industrial crops is made by precipitation at the beginning (r = 0.50, r = 0.44, and r = 0.52, respectively) and in the middle of the growing season (r = 0.55, r = 0.76, and r = 0.51, respectively). Temperature and precipitation during the growing season have a complex effect on cereals. This is proven by correlations with HCS and the Ped index (r = 0.45 and r = −0.56, respectively). Aridity at the beginning of the growing season affects the yield of oilseeds and potatoes. This is confirmed by correlations with the Ped index (r = −0.49 and r = −0.52, respectively). In general, the aridity of the growing season has a significant impact on the yield of cereals (r = −0.57). Negative relationships have been found between the air temperature growing season and the yield of potatoes (r = −0.50) and cereals (r = −0.53). The results of the study were compared with data from the Copernicus Climate Change Service database. We identified climate trends under RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5 scenarios. These scenarios should be taken into account when developing plans for the adaptation of agriculture in the Republic of Bashkortostan to changes in the regional climate. Maximum decrease in precipitation is established for the RCP 6.0 scenario. This can have an extremely negative impact on crop yields. This problem is especially relevant for the southern part of the Republic of Bashkortostan. The information presented in the study will allow for a more effective adaptation of the agricultural sector to current and future climate changes. [ABSTRACT FROM AUTHOR]

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Simple Summary: Butterflies are in decline due to habitat conversion, climate change, and agricultural practices. Insects have a narrow range of habitable conditions, and extreme fluctuations in weather or climate can lead to population declines. Other studies have shown that butterfly populations are declining in western US; however, given the distinct climate of the southwest, with two distinct rainy seasons, we wanted to explore whether the general trends held true for Arizona. We used 40 years of community science data taken at 13 sites and in two seasons to assess population trends and relationships with climate variables (precipitation and temperature). We found that the declines seen in Arizona reflect the general trends found for western US. The preceding winter precipitation is a driver of both the number of species in the fall and spring surveys and the total number of unique butterflies in the spring surveys. The number of unique butterflies counted in the fall surveys was more affected by summer monsoon precipitations. Managers and policy makers may use these results, combined with climate projections, to consider implementing management actions to help counteract butterfly declines, such as creating more refugia for butterflies across the landscape. Butterfly populations are declining worldwide, reflecting our current global biodiversity crisis. Because butterflies are a popular and accurate indicator of insect populations, these declines reflect an even more widespread threat to insects and the food webs upon which they rely. As small ectotherms, insects have a narrow range of habitable conditions; hence, extreme fluctuations and shifts caused by climate change may increase insects' risk of extinction. We evaluated trends of butterfly richness and abundance and their relationship with relevant climate variables in Arizona, U.S.A., using the past 40 years of community science data. We focused on precipitation and temperature as they are known to be influential for insect survival, particularly in arid areas like southwestern U.S.A. We found that preceding winter precipitation is a driver of both spring and summer/fall butterfly richness and spring butterfly abundance. In contrast, summer/fall butterfly abundance was driven by summer monsoon precipitations. The statistically significant declines over the 40-year period were summer/fall butterfly abundance and spring butterfly richness. When controlling for the other variables in the model, there was an average annual 1.81% decline in summer/fall season butterfly abundance and an average annual decline of 2.13 species in the spring season. As climate change continues to negatively impact winter precipitation patterns in this arid region, we anticipate the loss of butterfly species in this region and must consider individual butterfly species trends and additional management and conservation needs. [ABSTRACT FROM AUTHOR]