Malaria transmission is associated with climatic variability and vector control interventions, and understanding their long-term and lagged associations is critical in regions approaching elimination. This 23-year retrospective study (2001-2023) examined associations between climatic factors and malaria incidence in eight base counties of Sistan and Baluchestan Province, southeast Iran. Negative binomial and zero-inflated Poisson regression models were applied to account for overdispersion and excess zeros, incorporating 1-3 month lagged exposures. Seasonal patterns were assessed using linear mixed-effects models, and the impact of indoor residual spraying (IRS) population coverage (2013-2023) was evaluated using a negative binomial generalized linear model. Malaria incidence declined during the elimination phase but resurged in 2022-2023. Across counties analysed with negative binomial models, a 1 degrees C increase in mean temperature (1-3 month lag) was associated with a similar to 16% increase in incidence (IRR = 1.16), highlighting a consistent positive effect. Relative humidity showed heterogeneous but generally positive associations, whereas precipitation effects were weak and inconsistent. Incidence was higher in spring (4.6-fold), summer (7.9-fold) and autumn (6.8-fold) compared with winter. Increased IRS population coverage was positively associated with malaria incidence (IRR = 4.15 per 10% increase; 95% CI: 2.06-8.34), likely reflecting reactive spraying in response to higher transmission. Malaria transmission in southeast Iran is shaped by temperature-driven climatic variability and seasonal dynamics. Programmatic vector control responds to changes in transmission, emphasizing the need for integrated, climate-informed planning. Further research incorporating lagged predictive modelling and human mobility data is warranted to enhance elimination strategies.

Malaria transmission is influenced by climatic and environmental conditions, but the relative contributions of large-scale climate variability and local environmental factors remain unclear in southeastern Iran. We assessed associations between ENSO, remotely sensed environmental indicators, monsoon precipitation, and malaria incidence during 2001–2023. Monthly malaria surveillance data from eight counties in southeastern Iran (2001–2023) were analyzed using a retrospective ecological longitudinal design. Predictors included Niño 3.4 ENSO, NDVI, NDWI, and LST, with candidate lags evaluated based on model fit, estimated effects, and epidemiological plausibility. The primary analysis used a negative binomial mixed-effects model (NB-GLMM) with a log link, population offset, calendar-month adjustment, a four-degree-of-freedom nonlinear temporal trend, and county-level random intercepts. A quadratic term for six-month lagged LST was included to assess nonlinearity. County-specific models and random-slope analyses were used to assess spatial heterogeneity, while monsoon precipitation was analyzed separately using cumulative June–September rainfall in Chabahar, Saravan, and Sarbaz. In the final province-wide model, six-month lagged LST showed a significant nonlinear, U-shaped association with malaria incidence, with the estimated minimum occurring at approximately 0.32 standard deviations above the mean LST. ENSO (IRR = 1.01, 95

Cutaneous leishmaniasis (CL) represents a significant vector-borne disease in Iran. Our study examined the status of zoonotic CL (ZCL) in the country and forecasted the influence of global climate change on the monthly activity of Phlebotomus papatasi, the main vector of ZCL in the country. To predict the impact of climate change on the monthly activity of Ph. papatasi, we obtained the monthly average minimum and maximum temperatures for both the reference and future periods, using the MIROC6 model and two different shared socioeconomic pathway (SSP) scenarios. Based on our analysis, we found that Ph. papatasi can be active in Iran from March to November, although this may vary depending on the region. Our predictions suggest that the duration of Ph. papatasi's activity may change following future changes in weather patterns. In different scenarios, the duration of the active season in various regions of the country extends by at least 1-2 months. This extension is likely more pronounced in the southern provinces. Additionally, our findings indicate a notable correlation between ZCL incidence, the presence of Ph. papatasi, and environmental factors in Ardestan, Esfahan Province. This study focuses on the impact of temperature on the activity and distribution of Ph. papatasi in Iran, which is a significant vector for transmitting ZCL. The study predicts that with future climate scenarios, especially SSP5-8.5, the activity of this vector will start earlier, last longer, and might even occur throughout the year by the 2050s, thereby increasing the risk of ZCL transmission. Although temperature plays a dominant role in shaping the activity of Ph. papatasi, its influence is not consistent across Iran. The variation in different regions emphasizes the importance of implementing targeted public health approaches to address the changing risks of ZCL transmission due to evolving climate conditions. However, it acknowledges that certain factors such as land use and humidity have not been taken into account and requests additional research in these areas. It also calls for enhanced environmental monitoring and public health interventions.

This study investigated the effect of extreme precipitation values on the trend of vegetation cover change in southeastern Iran. Extreme precipitation indices were selected using a fixed approach recommended by the Expert Team on Climate Change Detection and Indices, according to local needs and theclimatic characteristics of the study area. These indices were extracted using daily precipitation data from the stations of the study area over a 21 years from 2000 to 2020 utilizing RclimDex software. The NDVI index of MODIS satellite images was used to analyze the trend of annual vegetation cover changes. Also, the multilayer perceptron neural network method was used to examine the relationship between changes in extreme precipitation indices and vegetation cover changes. The results showed that the NDVI index has an increasing trend spatially from the east to the west of the region. Areas with low NDVI were observed in the eastern, southern, and northern parts and areas with high NDVI were observed in the western part of the study area. The trend of NDVI index changes was significant and upward only at Rudan station (P-value = 0.01) and Kahnuj station (P-value = 0.0001). The overall trend indicated an increase in precipitation extremes during the study period. The extreme precipitation indices Rx1, SDII, and PRCPTOT have the most significant on the trend of changes in vegetation cover in the southeastern region of Iran. A comprehensive study of the impact and trend of changes in extreme precipitation indices on vegetation cover in the southeastern region of Iran will lead to the development of sustainable management methods and resilience to climate change.

Climate changes have a significant effect on dust extremes. Dust extremes in humid ambient air can simultaneously or successively form wet mud deposition on the surface of urban areas. The mud deposition on the power network systems and devices causes irreversible damage and significantly influences system performance and efficiency in southwest Iran. This often results in blackouts that cause problems in the operation of urban infrastructure and people's daily activities for up to several days. Khuzestan province was chosen as the case study in this study, and the climatic conditions and risk assessment of mud formation in this area were investigated. Data on a diurnal and monthly timescale of dust and humidity level was used for assessing extreme dust and wet conditions. The data was taken from Khuzestan synoptic station 8 over 11 years (2009-2019). The multivariate copula-based framework is used to calculate univariate and bivariate return periods of mud deposition hazard. The results imply that dust anomalies increase the probability of dust extreme coincidence with wet extreme and occurrence of wet mud hazards in the cold seasons of the year. In addition, limited adaptive capacity, shortage of information, and poor coordination and cooperation by the authorities caused the large-scale impact of the wet mud hazard in Khuzestan. Considering only relative humidity data, the return period of 2017 Khuzestan mud adhesion hazard is approximately 12 to 43 years. If we consider only dust, the return period of 2017 Khuzestan mud adhesion hazard is estimated at 80 to 700 years. However, for both dust and relative humidity extremes, the joint return periods for TDR (Dust and Relative humidity) and T'DR (Dust or Relative humidity) are respectively estimated greater than 200 and lower than 20 years.

Climate change is expected to cause important changes in precipitation patterns in Iran until the end of 21st century. This study aims at evaluating projections of climate change over Iran by using five climate model outputs (including ACCESS-ESM1-5, BCC-CSM2-MR, CanESM5, CMCC-ESM2 and MRI-ESM2-0) of the Coupled Model Intercomparison Project phase 6 (CMIP6), and performing bias-correction using a novel combination of quantile mapping (QM) and random forest (RF) between the years 2015 and 2100 under three shared socioeconomics pathways (SSP2-4.5, SSP3-7.0 and SSP5-8.5). First, bias-correction was performed on ERA5-Land reanalysis data as reference period (1990-2020) using the QM method, then the corrected ERA5-Land reanalysis data was considered as measured data. Based on the corrected ERA5-Land reanalysis data (1990-2020) and historical simulations (1990-2014), the future projections (2015-2100) were also bias-corrected utilizing the QM method. Next, the accuracy of the QM method was validated by comparing the corrected ERA5-Land reanalysis data with model outputs for overlapping years between 2015 and 2020. This comparison revealed persistent biases; hence, a combination of QM-RF method was applied to rectify future climate projections until the end of the 21st century. Based on the QM result, CMCC-ESM2 revealed the highest RMSE in both SSP2-4.5 and SSP3-7.0 amounting to 331.74 and 201.84 mmyear(-1), respectively. Particularly, the exclusive use of the QM method displayed substantial errors in projecting annual precipitation based on SSP5-8.5, notably in the case of ACCESS-ESM1-5 (RMSE = 431.39 mmyear(-1)), while the RMSE reduced after using QM-RF method (197.75 mmyear(-1)). Obviously, a significant enhancement in results was observed upon implementing the QM-RF combination method in CMCC-ESM2 under both SSP2-4.5 (RMSE = 139.30 mmyear(-1)) and SSP3-7.0 (RMSE = 151.43 mmyear(-1)) showcasing approximately reduction in RMSE values by 192.43 and 50.41 mmyear(-1), respectively. Although each bias-corrected model output was evaluated individually, multi-model ensemble (MME) was also created to project the annual future precipitation pattern in Iran. By considering that combination of QM-RF method revealed the lower errors in correcting model outputs, we used the QM-RF technique to create the MME. Based on SSP2-4.5, the MME climate projections highlight imminent precipitation reductions (>10%) across large regions of Iran, conversely projecting increases ranging from 10% to over 20% in southern areas under SSP3-7.0. Moreover, MME projected dramatic declines under SSP5-8.5, especially impacting central, eastern, and northwest Iran. Notably, the most pronounced possibly decline patterns are projected for arid regions (central plateau) and eastern areas under SSP2-4.5, SSP3-7.0 and SSP5-8.5.

Climate change has increased the exposure risk of workers to occupational health risk factors and diseases. This study aims to investigate the impacts of climate change on the occupational health indicators at the workplaces in Iran. This study was conducted during 2021 in three climatic regions of Iran. Required data was collected from Health Deputies of Medical Universities and Iran Meteorological Organization. Stepwise linear regression model used for data analysis and predictions were done based on three scenarios of SSP1-2.6, SSP3-7.0, and SSP5-8.5 during the period of 2021-2100. This indicated 2.6 and 2.9 times higher percentage of workers who were exposed to heat stress and Ultra Violet (UV) radiation, respectively, in the provinces understudy. This study suggests a holistic approach to address potential impacts of climate change on workers' health and safety that would benefit in making decisions on climate-related planning and developing the adaptation strategies at workplaces.

Changes in climate have a significant effect on dust extremes. Occurring dust extremes in humid ambient air simultaneously or successively can form wet mud deposition on the surface of urban areas. The mud deposition on the power network systems devices causes irreversible damage to the devices and significantly influences system performance and efficiency in southwest Iran. This often resulted in blackouts that have caused problems in the operation of urban infrastructure and people's daily activities for up to several days. In this study, the climatic conditions and risk assessment of mud formation were investigated in Ahvaz. Data on a diurnal and monthly timescale of dust and humidity relative used for assessing extreme dust and wet conditions and were taken from Ahvaz synoptic station over 11 years (2009-2019). The multivariate copula-based framework is used to calculate univariate and bivariate return periods of mud deposition hazard. The results imply that dust anomalies increase the probability of dust extreme coincidence with wet extreme and occurrence of wet mud hazards in the cold seasons of the year. In addition, limited adaptive capacity, shortage of information, and poor stakeholder coordination and cooperation caused the large-scale impact of wet mud hazard in Ahvaz. By considering only relative humidity data, the return period of the 2017 Ahvaz mud adhesion hazard, is approximately 12 to 43 years. If we consider only dust the return period of the 2017 Ahvaz mud adhesion hazard is estimated at 80 to 700 years. However, for both dust and relative humidity extreme, the joint return periods for T DR (Dust and Relative humidity) and T' DR (Dust or Relative humidity) are Respectively estimated greater than 200 and lower than 20 years.

Climate change will affect the distribution of species in the future. To determine the vulnerable areas relating to CL in Iran, we applied two models, MaxEnt and RF, for the projection of the future distribution of the main vectors and reservoirs of CL. The results of the models were compared in terms of performance, species distribution maps, and the gain, loss, and stable areas. The models provided a reasonable estimate of species distribution. The results showed that the Northern and Southern counties of Iran, which currently do not have a high incidence of CL may witness new foci in the future. The Western, and Southwestern regions of the Country, which currently have high habitat suitability for the presence of some vectors and reservoirs, will probably significantly decrease in the future. Furthermore, the most stable areas are for T. indica and M. hurrianae in the future. So that, this species may remain a major reservoir in areas that are present under current conditions. With more local studies in the field of identifying vulnerable areas to CL, it can be suggested that the national CL control guidelines should be revised to include a section as a climate change adaptation plan.

Net primary production is a sensitive index to changes in climatic factors and human activities. The present study was conducted with the aim of quantitatively evaluating the relative role of climate change and human activities on the development of desertification in Torbat Heydarieh and Bojnoord based on the spatio-temporal variations of net primary production. First, primary net production was estimated by ground measurement. Subsequently, it was simulated using the CASA model in statistical period between 1986 and 2017. Six scenarios were designed to determine the relative role of climate change and human activities in the expansion or inversion of desertification. The results of the research showed that net primary production has declined over the 31-year period and that the trend in its changes is negative. An examination of different desertification scenarios showed that the city of Bojnord is under the scenario of expansion of desertification due to climate change, and Torbet Heydarieh city is under the scenario of desertification expansion due to the interaction between climate change and human activities. Based on this, the trend of changes in actual and potential primary net production in Bojnord and Torbat Heydarieh cities was negative and its intensity was classified in the low to medium decline category. Also, the slope trend of changes in primary net production caused by human activities in Torbat Heydarieh city was positive and in the low to medium increase class, and in Bojnord city, it was negative and in the low decrease class.The study of the development or return of desertification scenarios showed that 61.23% of the total area of the study area was affected by the expansion and development of desertification due to the impacts of climatic factors, and 38.77% of those affected by the interaction of climatic factors and human activities in expansion of desertification.

The climatological parameters can be different in various geographical locations. Moreover, they have possible impacts on COVID-19 incidence. Therefore, the purpose of this systematic review article was to describe the effects of climatic variables on COVID-19 pandemic in different countries. Systematic literature search was performed in Scopus, ISI Web of Science, and PubMed databases using (“Climate” OR “Climate Change” OR “Global Warming” OR “Global Climate Change” OR “Meteorological Parameters” OR “Temperature” OR “Precipitation” OR “Relative Humidity” OR “Wind Speed” OR “Sunshine” OR “Climate Extremes” OR “Weather Extremes”) AND (“COVID” OR “Coronavirus disease 2019” OR “COVID-19” OR “SARS-CoV-2” OR “Novel Coronavirus”) keywords. From 5229 articles, 424 were screened and 149 were selected for further analysis. The relationship between meteorological parameters is variable in different geographical locations. The results indicate that among the climatic indicators, the temperature is the most significant factor that influences on COVID-19 pandemic in most countries. Some studies were proved that warm and wet climates can decrease COVID-19 incidence; however, the other studies represented that warm location can be a high risk of COVID-19 incidence. It could be suggested that all climate variables such as temperature, humidity, rainfall, precipitation, solar radiation, ultraviolet index, and wind speed could cause spread of COVID-19. Thus, it is recommended that future studies will survey the role of all meteorological variables and interaction between them on COVID-19 spread in specific small areas such as cities of each country and comparison between them.

The climate science and applications communities need a broad and demand-driven concept to assess physical climate conditions that are relevant for impacts on human and natural systems. Here, we augment the description of the "climatic impact-driver" (CID) approach adopted in the Working Group I (WGI) contribution to the Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report. CIDs are broadly defined as "physical climate system conditions (e.g., means, events, and extremes) that affect an element of society or ecosystems. Depending on system tolerance, CIDs and their changes can be detrimental, beneficial, neutral, or a mixture of each across interacting system elements and regions." We give background information on the IPCC Report process that led to the development of the 7 CID types (heat and cold, wet and dry, wind, snow and ice, coastal, open ocean, and other) and 33 distinct CID categories, each of which may be evaluated using a variety of CID indices. This inventory of CIDs was co-developed with WGII to provide a useful collaboration point between physical climate scientists and impacts/risk experts to assess the specific climatic phenomena driving sectoral responses and identify relevant CID indices within each sector. The CID Framework ensures that a comprehensive set of climatic conditions informs adaptation planning and risk management and may also help prioritize improvements in modeling sectoral dynamics that depend on climatic conditions. CIDs contribute to climate services by increasing coherence and neutrality when identifying and communicating relevant findings from physical climate research to risk assessment and planning activities.

Land use and land cover (LULC) is one of the most important factors that affect the desertification risk in this study, the desertification sensitivity of the Mokhtaran basin in South Khorasan province was estimated by integrating LU/LC scenarios with the MEDALUS environmentally sensitive areas (ESAs) model to predict the desertification risk. The four main MEDALUS criteria including soil, climate, vegetation, and management were examined to assess the sensitive areas to the desertification. Land use maps were categorized using the Landsat satellite imageries of TM, ETM+, and OLI sensors for 1987, 1998, 2003 as a past scenario, and 2015 as a current scenario. Land use maps for 2025 and 2035 were produced as the future scenario based on the simulation of CA-Markov models. The validation results confirmed the model accuracy by calculating the kappa coefficient of 0.95. The land use map was predicted for the years 2025 and 2035 based on the transition rules and a transition area matrix. The results showed that the rainfed area was reduced by 68.29 km2 and the agricultural land was increased by 25.35 km2 during theperiod. In the protection area of playa-bare lands, the changes showed this area was increased by 26.86 km2. The rangeland has also experienced positive changes with an increase of 18.83 km2. Compared to the current scenario, the desertification trend in the future scenario was positively predicted by increasing the area of critical areas from 30.9% to 48.7% over 20 years. The most susceptible lands to desertification were known as playa-bare lands.

The involvement of early career researchers (ECRs) has been limited during the past Intergovernmental Panel on Climate Change (IPCC) assessment cycles. We conducted a global survey among ECRs and interviewed key experts of the IPCC process. Our results show that ECRs are highly motivated to become actively involved in the IPCC process but face a number of barriers to contribute. Mutually beneficial ways forward on how ECRs could contribute are outlined here, and recommendations to implement these paths are suggested to IPCC as well as to ECRs. Concluding, we show that ECRs have great potential to actively contribute to the IPCC process for the continuity of the IPCC as well as to climate science in general.

Iran is located in one of the Earth’s dry belts. Subtropical high-pressure and inland deserts have created arid and semiarid conditions for about 75% of the country’s area. In addition to this natural dryness, recent global climate change has also affected precipitation in different parts of Iran in terms of both mean and extreme amounts. Precipitation extremes can have many negative effects on different parts of the Earth’s ecosystems. The possible pattern of precipitation trends during recent years merits further study given the potentially important impacts on agriculture and water resources in Iran. Globally, observations show significant changes in the characteristics of extreme precipitation. To study recent trends in mean and extreme precipitation events in Iran, we examined a number of mean and extreme precipitation indices for a 58-year period (1960–2017) for 33 synoptic stations throughout the country. Daily precipitation data were collected from the Iranian Meteorological Organization for this period for all stations. Nine precipitation-based indices (one mean and eight extreme) were employed to study precipitation trends. The results showed that most areas have undergone a significant increasing trend in extreme precipitation values, precipitation intensity, and precipitation frequency. Significant positive trends were seen in the southwestern regions of Iran and the coasts of the Persian Gulf for the same precipitation indices. The results of this study could be used by managers and experts for long-term planning.

Due to the interaction of Tree species and its environment, descriptions and analysis of forest types are necessary. The aim of present study was to evaluate modeling distribution of forest types using parametric and nonparametric algorithm. Current research was carried out in Ziarat forestry plan, Golestan province, Iran. 556 samples were taken to measure the quantitative parameters of trees including tree height, diameter at the breast height and type of species via Systematic- Randomize pattern with 150×200 m. After that, the forest types have been determined according to frequency of species. Subsequently, the map of forest types have been produced using Physiographic factors (elevation, slope and aspect), Climate factor (rain fall, evaporating and temperature) via Parametric algorithm (Logistic Regression (LR)), Nonparametric algorithm (Artificial Neural Network (ANN)). The results showed that based LR and ANN, the largest area of forest type was observed in Fageto - Carpinetum with Parrotia persica (23.32%) followed by Fageto –Carpinetum (24.69%). In both methods, the elevation and rainfall events have been recognized as impotent factors. Regarding the limitation of input data and complexity of forest ecosystem, the result of LR and ANN are acceptable. Generally, ANN was more effective compared to LR. However, both algorithms are recommended in distribution mapping of forest type.

In recent years, global climate change has had significant biological, temporal, and spatial effects on many terrestrial habitats. The objective of this study was to evaluate the effect of climate change on the geographic distribution of Juniperus excelsa and prioritize its habitats for protection against these effects until 2070. The study was conducted using the MaxEnt species distribution model and two data series GFDL-CM3 and MRI-CGCM3 under scenarios RCP2.6 and RCP4.5 of the 5th IPCC report. Our results revealed that elevation, minimum temperature of coldest month, precipitation of coldest quarter, annual mean temperature, and slope aspect, in that order, have the greatest effects on the species’ distribution in the study area. Under optimistic scenario RCP2.6, both models predicted that the species’ presence area will grow, but under RCP4.5, models predicted that by 2070, some parts of its habitat in western and central heights will be lost because of change in climate parameters like minimum temperature of coldest month and precipitation of coldest quarter. Under the latter scenario, the northeastern parts of the study area showed no changes in terms of climatic parameters and climatic niche. The results of both climate data series indicated that the Juniperus excelsa will slowly migrate to higher elevations to cope with the changing climate. Assessment of the results through field studies showed that outputs of GFDL-CM3 are closer to the reality.

Climate extremes can have many negative effects on different sectors.Globally, observations show significant changes in the characteristics of extreme events.We examined trends in extreme temperature and precipitation indices in Iran during the period 1960-2014.We present results from 33 quality controlled and homogenous synoptic stations (excluding stations with excessive missing data).For each station, we calculate 27 indices characterizing extreme temperature and precipitation.For all indices (including Rx5DAY, CWD, R95p, R99p, GSL and TXn) positive, negative, and insignificant trends were obtained.Generally, there were negative trends in R10mm, R20mm, R25mm, CDD, PRCPTOT, FD, TN10p, TX10p, CSDI and positive trends in Rx1DAY, SDII, SU25, TR20, TXx, TNn, TNx, TN90p, TX90p and WSDI at most stations.There is a decreasing trend in the magnitude and frequency of cold extremes and an increasing trend in magnitude and frequency of warm extremes over the observational record, which is consistent with previous research reporting the warming trends of the climate.