Crop cultivation is intrinsically linked to human well-being, and the timely identification of crop types is critical for agricultural monitoring and food security assessment. However, without extensive field surveys, achieving timely, large-scale, high-resolution crop identification remains challenging. This study proposes an intelligent crop identification strategy (ICIS) that integrates crop phenological development information with a model transfer strategy based on machine learning, generating 10-meter resolution crop distribution maps of major crop types from 2020 to 2023 in Northeast China (NE). ICIS utilizes time series data from Sentinel-1 (S1) and Sentinel-2 (S2) to extract multi-source temporal features covering the full growth cycles of maize, rice, and soybeans. A Random Forest (RF) classifier was trained on representative sample sites, and model transfer was employed to predict crop distribution across different years and regions. The experimental results show that the models achieved F1-scores above 0.92 across provinces, peaking at 0.97. For rice, the F1-scores for Jilin, Heilongjiang, and Liaoning remained stable at 0.94. Maize F1-scores consistently exceeded 0.92, and although the soybean accuracy varied spatially, it reached 0.97 in certain areas. Comparison with crop area statistics from the China Statistical Yearbook at the city level revealed that the average root mean square error (RMSE) across four years for the three crops was 8.6 & times; 104 ha, with coefficients of determination (R2) consistently above 0.90, reaching up to 0.96. These results demonstrated the strong generalizability and robustness of the proposed ICIS Strategy for crop mapping across both temporal and spatial scales. Moreover, this study reveals the complementary strengths and key contributions of optical and Synthetic Aperture Radar (SAR) time series features in modeling crop growth dynamics. Overall, the ICIS provides an efficient and reliable technical means for the rapidly acquiring large-scale crop information. It provides robust support for agricultural monitoring and food security assessment, demonstrates significant potential for large-scale agricultural applications, and offers valuable insights and technical support for remote sensing data processing, image analysis, and machine learning-based agricultural automation and decision-making.

Highlights What are the main findings? The hazard weight of foreign fire sources in spring and summer is higher than that of other fire sources in Inner Mongolia. The probability of grassland fire occurrence exhibited a decreasing trend from east to west, with a relatively high incidence in the eastern region and the lowest probability in the western region of Inner Mongolia. What is the implication of the main finding? This study proposes a differentiated governance strategy for forest and grassland fires, marking a paradigm shift from "reactive firefighting" to "proactive governance." A comprehensive seasonal grassland fire hazard assessment model was constructed using fire source, fuel, and environmental hazard indices. This model was employed to evaluate seasonal fire hazard zoning in Inner Mongolia, thereby providing an important theoretical basis for the scientific management and effective prevention and control of grassland fires.Highlights What are the main findings? The hazard weight of foreign fire sources in spring and summer is higher than that of other fire sources in Inner Mongolia. The probability of grassland fire occurrence exhibited a decreasing trend from east to west, with a relatively high incidence in the eastern region and the lowest probability in the western region of Inner Mongolia. What is the implication of the main finding? This study proposes a differentiated governance strategy for forest and grassland fires, marking a paradigm shift from "reactive firefighting" to "proactive governance." A comprehensive seasonal grassland fire hazard assessment model was constructed using fire source, fuel, and environmental hazard indices. This model was employed to evaluate seasonal fire hazard zoning in Inner Mongolia, thereby providing an important theoretical basis for the scientific management and effective prevention and control of grassland fires.Abstract In recent years, global climate change has significantly increased the incidences of grassland fires, shifting their occurrence from seasonal events (primarily spring and autumn) to annual incidents. To enable a more accurate evaluation and zoning of grassland fire risk, this study established the Fire Source Hazard Index, Fire Fuel Hazard Index, and Fire Environmental Hazard Index based on multi-source data, employing the entropy weight method, random forest modeling, mathematical statistics, and spatial analysis. A comprehensive seasonal grassland fire hazard assessment model was constructed using these three indices and seasonal fire hazard zones were evaluated in Inner Mongolia. The results indicated that, among the fire source factors, the hazard weight of foreign fire sources was relatively high during spring (0.37) and summer (0.44). In autumn and winter, the hazard weights of road networks were higher, at 0.38 and 0.44, respectively. In the comprehensive hazard assessment, the fire environment hazard exhibited an objective existence with notable seasonal variation, whereas the hazard weight of fire source factors exceeded that of fuels across all seasons. The comprehensive grassland fire hazard in Inner Mongolia demonstrated distinct seasonality and regional heterogeneity. Temporally, fire hazards are widespread and intense in spring, limited and concentrated in summer, extensive yet dispersed in autumn, and lowest in winter. Spatially, grassland fire hazards decreased from east to west, with higher hazards concentrated in the eastern regions. Western Inner Mongolia had the lowest probability of fire occurrence. The validation results revealed a positive correlation between the proportion of fire points and hazard grades, confirming the rationality of the hazard classification and the accuracy of the assessment, which provides an important theoretical basis for the scientific management and effective prevention and control of grassland fires. Future research should further refine and explore more precise methods for grassland fire hazard assessment.

Leaf chlorophyll content (LCC) is a key indicator of crop growth condition. Real-time, non-destructive, rapid, and accurate LCC monitoring is of paramount importance for precision agriculture management. This study proposes an improved method based on multi-source data, combining the Sentinel-2A spectral response function (SRF) and computer algorithms, to overcome the limitations of traditional methods. First, the equivalent remote sensing reflectance of Sentinel-2A was simulated by combining UAV hyperspectral images with ground experimental data. Then, using grey relational analysis (GRA) and the maximum information coefficient (MIC) algorithm, we explored the complex relationship between the vegetation indices (VIs) and LCC, and further selected feature variables. Meanwhile, we utilized three spectral indices (DSI, NDSI, RSI) to identify sensitive band combinations for LCC and further analyzed the response relationship of the original bands to LCC. On this basis, we selected three nonlinear machine learning models (XGBoost, RFR, SVR) and one multiple linear regression model (PLSR) to construct the LCC inversion model, and we chose the optimal model to generate spatial distribution maps of maize LCC at the regional scale. The results indicate that there is a significant nonlinear correlation between the VIs and LCC, with the XGBoost, RFR, and SVR models outperforming the PLSR model. Among them, the XGBoost_MIC model achieved the best LCC inversion results during the tasseling stage (VT) of maize growth. In the UAV hyperspectral data, the model achieved an R2 = 0.962 and an RMSE = 5.590 mg/m2 in the training set, and an R2 = 0.582 and an RMSE = 6.019 mg/m2 in the test set. For the Sentinel-2A-simulated spectral data, the training set had an R2 = 0.923 and an RMSE = 8.097 mg/m2, while the test set showed an R2 = 0.837 and an RMSE = 3.250 mg/m2, which indicates an improvement in test set accuracy. On a regional scale, the LCC inversion model also yielded good results (train R2 = 0.76, test R2 = 0.88, RMSE = 18.83 mg/m2). In conclusion, the method proposed in this study not only significantly improves the accuracy of traditional methods but also, with its outstanding versatility, can achieve rapid, non-destructive, and precise crop growth monitoring in different regions and for various crop types, demonstrating broad application prospects and significant practical value in precision agriculture.

Global climate change exerts a profound influence on regional vegetation dynamics. Investigating the spatial and temporal variations of the normalized difference vegetation index (NDVI) within watersheds and its responses to climate change is essential for supporting the development and management of regional ecological environments. This study analyzed spatial and temporal variations of NDVI during the vegetation growing season in Inner Mongolia, along with its responses to climate change, using slope analysis and correlation analysis. The study utilized monthly NDVI composite products from MODIS and meteorological data spanning 1999 to 2019. The key findings are: Inner Mongolia's spatial distribution of average NDVI values exhibited a distinct pattern, with values decreasing progressively from northeast to southwest. During the study period, the mean NDVI values in Inner Mongolia during the growing season demonstrated a significant upward trend, with an average annual increase of 0.0016. Over the 21-year period, temperature trends in Inner Mongolia remained relatively stable, whereas precipitation showed a significant increasing trend. The correlation between vegetation NDVI and climatic factors during the growing season varied significantly across regions. Precipitation was found to have a stronger and more consistent influence on vegetation growth compared to temperature, underscoring its pivotal role in the region. The interannual shift in the center of gravity of average NDVI values was minimal, predominantly confined to northeastern Inner Mongolia. These findings offer a valuable scientific basis for Inner Mongolia's ecological environment protection and restoration efforts.

China's rapid socioeconomic development and increasing consumption of resources has greatly intensified environmental pollution and ecological degradation across the country over recent decades. Regional differences and interactions among various elements enhance the complexity of addressing these issues, which not only makes resource and environmental management challenging, but also requires flexible and dynamic management strategies to cope with multilevel and multidimensional conflicts and uncertainties. Moreover, effective management of resources and environmental impacts requires the integration of numerous factors and adaptative strategies to respond to the continuously changing environmental conditions and evolving societal needs. Here, we introduce a conceptual framework and evaluation index system for resource and environmental carrying capacity (REECC) from "production-living-ecology" spaces (PLES) perspective. We used this framework to quantitatively evaluate REECC in the western Jilin Province and to conduct in-depth analyses of its internal drivers and climate impacts. The REECC and direction of movement of the center of gravity of different land-use types were also assessed. Spatial and temporal changes in REECC in this region over the period of 2005-2020 were particularly striking. In 2005, areas with an REECC between 0.4 and 0.5 accounted for 47.89% of total areas, but this fell to 37.16% in 2010 before leveling off at 41.76% in 2015-2020. Population density was the primary influencing factor, followed by EVI and PM2.5. Precipitation was significantly positively correlated with REECC, whereas temperature was significantly negatively correlated with REECC. The direction of the REECC trend was found to be consistent with that for grasslands, and the position of the center of gravity was shifted.

Changes in forest gross primary productivity (GPP) are essential for understanding the dynamics of the global carbon cycle and impacts of climate change. Given the increasing frequency of droughts and wildfires due to climate change, assessing their impact on GPP in boreal forests has become increasingly important. This study utilized remote sensing data, including GPP data, the standardized precipitation-evapotranspiration index (SPEI), and burned area data from 2001 to 2022, and employed trend analysis, Pearson correlation analysis, and Shapley Additive Explanations (SHAP values) to examine the spatiotemporal characteristics of GPP in boreal forests and their response mechanisms to drought and wildfires. The results were as follows. (1). GPP in boreal forests exhibited a spatial pattern of being higher in the south and lower in the north, with approximately 54.14 % of the area exhibiting an increasing trend in GPP and approximately 41.0 % showing a decreasing trend. (2) Areas with a declining GPP highly overlapped with regions that experienced intense drought. The SPEI primarily showed a negative correlation with GPP; however, the proportion of positively correlated areas increased with longer SPEI time scales. (3) Wildfires generally exerted a negative impact on vegetation GPP, as burned areas typically decreased in productivity compared to unburned regions; however, most affected areas demonstrated a basic recovery in GPP within five years. (4) Shrublands, grasslands, and savannas exhibited a higher GPP sensitivity to drought and wildfires, whereas forest communities demonstrated greater ecological resilience. (5) The interaction between drought and wildfires exacerbated negative impacts on GPP over short-term time scales; however, a gradual weakening influence was observed over medium- to long-term timescales. This study offers novel insights into and a theoretical foundation for assessing and understanding the impacts of drought and wildfires on the GPP of boreal forests.

Frequent wildfires in the eastern grasslands of Mongolia pose significant threats to the ecological environment and pastoral livelihoods, creating an urgent need for high-temporal-resolution and high-precision fire prediction. To address this, this study established a daily-scale grassland fire risk assessment framework integrating multi-source remote sensing data to enhance predictive capabilities in eastern Mongolia. Utilizing fire point data from eastern Mongolia (2012–2022), we fused multiple feature variables and developed and optimized three models: random forest (RF), XGBoost, and deep neural network (DNN). Model performance was enhanced using Bayesian hyperparameter optimization via Optuna. Results indicate that the Bayesian-optimized XGBoost model achieved the best generalization performance, with an overall accuracy of 92.3%. Shapley additive explanations (SHAP) interpretability analysis revealed that daily-scale meteorological factors—daily average relative humidity, daily average wind speed, daily maximum temperature—and the normalized difference vegetation index (NDVI) were consistently among the top four contributing variables across all three models, identifying them as key drivers of fire occurrence. Spatiotemporal validation using historical fire data from 2023 demonstrated that fire points recorded on 8 April and 1 May 2023 fell within areas predicted to have “extremely high” fire risk probability on those respective days. Moreover, points A (117.36° E, 46.70° N) and B (116.34° E, 49.57° N) exhibited the highest number of days classified as “high” or “extremely high” risk during the April/May and September/October periods, consistent with actual fire occurrences. In summary, the integration of multi-source data fusion and Bayesian-optimized machine learning has enabled the first high-precision daily-scale wildfire risk prediction for the eastern Mongolian grasslands, thus providing a scientific foundation and decision-making support for wildfire prevention and control in the region.

Currently, regional ecological risk assessments (ERAs) of polycyclic aromatic hydrocarbons (PAHs) often fail to integrate socioeconomic and natural factors, leading to incomplete evaluations. This study developed a PAH ecological risk assessment model based on multi-source data fusion (including remote sensing, monitoring, socioeconomic, meteorological, and land use data). The model incorporates not only the hazards of risk sources but also the vulnerability of ecological receptors. Comparative validation analysis with traditional methods showed that integrating ecosystem carrying capacity parameters identified an overall moderate level of PAHs in the region. This approach more accurately captures spatial variations in ecological risk, providing stronger support for environmental management and decision-making. Additionally, a driving mechanism analysis using machine learning and structural equation modeling revealed that human activities are the primary drivers influencing PAHs ecological risk (λ = -0.89). This study offers a new framework for regional PAHs ERAs.

Accurate estimation of regional-scale evapotranspiration (ET) and reference evapotranspiration (ETo) is crucial for scientific and rational water resource management, agricultural irrigation decision-making, and ecosystem monitoring. Currently, the estimation of ETo or ET still mainly relies on observational data from surface meteorological stations or flux towers. However, due to the complexity of parameters and models, as well as the uneven distribution of observation stations, there is significant uncertainty in the estimation of ETo or ET. We have developed a semi-empirical model based on Fick's law and the optimal stomatal behavior model by combining vegetation photosynthesis indicators and meteorological parameters. We compared the potential of using solar-induced chlorophyll fluorescence (SIF), near-infrared reflectance of vegetation (NIRv), and the product of NIRv and photosynthetically active radiation (NIRvP) to estimate ETo and ET. The results indicate that NIRvPxVPD(0.5) (the 0.5th power of vapor pressure deficit) has an advantage in estimating ETo. Additionally, in nonlinear models, the accuracy of estimating ET using NIRvP and VPD0.5 surpasses that of using SIF. We also revealed that temperature and atmospheric pressure are the main factors mediating the relationship between NIRvPxVPD(0.5) and ETo, as well as between NIRvP and ET. The research results lay the foundation for providing more accurate and reliable methods for estimating vegetation evapotranspiration.

Salicornia europaea L. is a well-known model plant for studying the mechanism of salt tolerance. A substantial decline in the S. europaea population has been observed in the semi-arid steppe of the Mongolian Plateau. The relationship between environmental factors and its population dynamics in the grassland ecosystem remains inadequately investigated. Rhizosphere microbial communities, representing the most direct and influential biological factors affecting plant populations, have received limited research attention in the context of halophytes. Four density treatments of S. europaea (bare land—SEB, low density—SEL, medium density—SEM, and high density—SEH) in a single-factor randomized-block design with five replications were established to evaluate the relationship between rhizosphere soil bacterial communities and environmental factors. The results showed that as the density of S. europaea increased, the soil pH decreased, while available phosphorus increased. Rhizosphere soil bacterial communities associated with S. europaea populations in the saline-alkali wetland were dominated by Proteobacteria, Bacteroidota, Actinobacteria, Gemmatimonadota, and Halobacterota. Notably, the genera Antarcticibacterium, Wenzhouxiangella, BD2-11_terrestrial_groupBD2-11, Halomonas, and Natronorubrum were found to be particularly abundant. The Simpson index of the rhizosphere soil bacterial community in the S. europaea treatments was significantly higher than that in bare land. Soil pH and nitrate nitrogen were the primary environmental drivers of the rhizosphere bacterial community. Overall, the rhizosphere soil’s bacterial diversity in saline wetlands under a high-salt environment was not affected by the decrease in the S. europaea population. S. europaea plays an important role in shaping soil bacterial community structure through its influence on the surrounding soil environment. The cultivation of S. europaea is a phytoremediation strategy to improve soil salinization.

Ecological zoning is essential for optimizing regional ecological management and improving environmental protection efficiency. While previous studies have primarily focused on the independent analysis of land use intensity (LUI) and landscape ecological risk (LER), there has been limited research on their coupled relationship. This study, conducted in the Western Jilin (WJL), introduces an innovative ecological zoning method based on the Production–Living–Ecological Space (PLES) framework, which explores the interactions between LUI and LER, filling a gap in existing research. The method employs a coupling coordination degree (CCD) model and Geographic Information System (GIS) technology to construct an LUI-ERI coupling model, which is used to delineate ecological zones. The results indicate that: (1) The PLES in the study area is predominantly production space (PS), with the largest area of transfer being production ecological space (PES) 2784.23 km2, and the most significant transfer in being PS 3112.33 km2. (2) Between 2000 and 2020, both LUI and LER exhibited downward trends, with opposite spatial distribution characteristics. The “middle” intensity zone and “highest” risk zone were the dominant LUI and LER types, covering approximately 46% and 45% of the total area, respectively. (3) The coupling coordination degree between LUI and LER showed a polarized trend, with an overall upward trajectory from 2000 to 2020. (4) The ecological zoning of the WJL can be categorized into an ecological core protection (ECP) zone, ecological potential governance (EPG) zone, ecological comprehensive monitoring (ECM) zone, ecological optimization (EO) zone, and ecological restoration (ER) zone, with the ecological core protection area occupying 61.63% of the total area. This study provides a novel perspective on ecological zoning and offers a systematic scientific basis for regional ecological management and spatial planning.

The environmental risks posed by polycyclic aromatic hydrocarbons (PAHs) and the diversity of their anthropogenic origins make them a global issue. Therefore, it is of utmost significance for protecting the aquatic environment and the growth of neighboring populations to identify their possible origins and ecological risk. Here, we detail the contamination profiles of 15 PAHs found in the East Liao River's surface waters in Jilin Province and use the receptor model Absolute Principal Component Analysis - Multiple Linear Regression (APCS-MLR) and diagnostic ratios method to identify the primary potential sources of pollution. Based on the natural hazard risk formation theory (NHRFT), an ecological risk assessment (ERA) model for PAHs in the East Liao River was developed. The method assesses the ecological risk status of PAHs by integrating the risk quotient (RQ) approach and the DPSIRM (driving force, pressure, state, impact, response, management) conceptual framework. Total concentrations in the surface water body were between 396.42 and 624.06 ng/L, with an average of 436.99 ng/L. The source research revealed that coal, biomass, and traffic emission sources are the most likely PAH contributors to the East Liao River. The ERA found that the majority of the sites' locations of the study were at low risk for PAHs in surface water bodies (30.7 % and 32.2 %, respectively), while only a tiny percentage of sites were at high or very high risk (1.8 % and 13.6 %). The study results provide theoretical support for the East Liao River's ecological, environmental protection, and policy formulation.

Global environmental changes continuously result in plant migration from lower elevations or latitudes into alpine or arctic tundra ecosystems. In response to global environmental changes, the alpine shrubby tundra on the Changbai Mountain in northeastern China has been invaded by a low-elevation herb species Deyeuxia angustifolia over the past several decades. In this experiment, we studied the effects of D. angustifolia migration, Nitrogen (N) deposition (ambient N vs. N-addition of 10 g/m(2)/yr), and elevation (a higher elevation of 2200 m vs. a lower elevation of 2050 m a.s.l.) on soil properties and soil microbial communities to better understand the consequences of this migration for soil microbes and obtain feedback on the likelihood of further migration. We found that the migration of D. angustifolia decreased the soil available phosphorus (AP) and microbial biomass (particularly the biomass of Gram-positive bacteria (Gp) and Actinobacteria) at 2200 m a.s.l. N addition enhanced the availability of soil N, the nitrogen: phosphorous (N: P) ratio, and reduced the fungal: bacterial (F: B) ratio at 2200 m a.s.l. There was a higher Gram-positive: Gram-negative bacterial (Gp: Gn) ratio at 2200 m a.s. l. than those at 2050 m a.s.l. Our results suggest that the upward migration of D. angustifolia into the shrubby tundra combined with N deposition will substantially change the soil microbial community composition on the one hand, and result in a shortage of soil P on the other hand, especially at higher elevations. These changes may increase the competitive ability of D. angustifolia, and further benefit its migration, and suppress the shrubby species that currently exist in that habitat. This study helps to understand the mechanisms for the upward migration of D. angustifolia and facilitates the management of the Changbai alpine tundra in a changing world.

Ecological security early warning research is an effective tool for statistical unsustainable bottom line and watershed management. Recent research has shown that climatic elements influence the characteristics of ecological security and its variability and that landscape patterns constrain the basis of regional ecological security. Thus, there is a need to identify trends in ecological safety under the driving forces of climate and landscape pattern changes to enable early warning measures. In this study, a new ecological security early warning system (ESEW) was established, and a comprehensive index was constructed to assess the ESEW in the East Liaohe River Basin (ELRB), China during 2000–2020. The system used a geographic detector to reveal the internal influences, external drivers, and synergistic effects of ecological security warnings in the basin in different periods. A Bayesian network (BN) model was used to simulate the risk of different ecological security warnings occurring under different scenarios. The results showed that the ESEW level is higher in the northwestern part of the ELRB, and the area occupied by extreme warning decreased simultaneously as the area occupied by no warning decreased. The changes of ecological security warning situation in the ELRB are not only influenced by internal factors but also driven by external factors, and the synergy between the core factors and external factors can better explain the driving mechanism of ecological security alarm than a single factor. The BN model was more sensitive to the simulation of no warning and extreme warning. This study provided insights into the maintenance of the security and sustainable development of watershed ecosystems and new perspectives for watershed ESEW research.

Spatiotemporal changes in wet and dry spells had significantly contributed to climate‐related natural hazards in East Asia, especially in river basins undergoing warming. However, few studies have systematically analysed these changes for river basins at different latitudes. Herein, we investigated the observed and projected variations in wet and dry spells for East Asian river basins at different latitudes. We found that river basins at low latitudes (Yangtze and Pearl rivers) had obvious wet characteristics, whereas those at high latitudes (Amur and Yellow rivers) had obvious dry characteristics. In general, the river basins presented a ‘wet south but dry north’ spatial pattern. Long‐duration wet spells have been shown to be the primary contributor to total wet spells in low‐latitude river basins, while long‐duration dry spells have been found to be the primary contributor to total dry spells in high‐latitude river basins. Importantly, climate is changing from prolonged wet spells to short, intense wet spells in low‐latitude river basins, whereas the climate is changing from single, short dry spells to moderate, prolonged dry spells at high latitudes. These changes indicate a high probability of flash floods in river basins at low latitudes and a high probability of droughts at high latitudes. In the future, more temporally clustered heavy precipitation events and prolonged dry spells may lead to increasingly uneven precipitation in high‐latitude river basins and the Pearl River basin, especially under the higher shared socioeconomic pathway scenarios. In contrast, more frequent consecutive heavy precipitation events may aggravate the risk of flooding in the Yangtze River basin. These findings are expected to serve as a guide for flood and drought risk mitigation in East Asia under a warming climate.

Precipitation extremes occurring on consecutive days may be of crucial importance for the formation of extensive and long-lasting flooding. Changes in such consecutive extreme precipitation (CEP) events between different regions (i.e., dry and wet regions) still remain unclear in China, which may result in different impacts on human livelihoods and ecosystems. Here, the changes in CEP frequency between thy and wet regions of China were studied by utilizing an observation-based gridded precipitation dataset. We further determined the driving factors of the changes in CEP frequency by separating the effects of precipitation intensification and temporal clustering of daily extreme precipitation. Our results showed that the CEP frequency in dry regions increased faster (9.73%.decale(-1)) compared with wet regions (1.14%.decade(-1)) during the last similar to 60 years. The increasing precipitation intensity primarily (over 90%) resulted in the increases of CEP events. However, changes in the temporal clustering of daily extreme precipitation can benefit the effects of the changes in precipitation intensity in thy regions but can reverse these effects in wet regions. In dry regions, the regression relationship of precipitation intensity and temperature (7.38%.degrees C-1) was stronger than that in wet regions (3.44%.degrees C-1), suggesting that the same magnitude of warming would cause greater precipitation intensity and consequently cause more frequent CEP events in dry regions. Therefore, as the intensification of precipitation and the increasing temporal clustering of daily extreme precipitation, more CEP events may increase the flooding risk in dry regions over China with a stronger warming signal.

The diurnal temperature range (DTR) has significantly decreased in many land areas as a consequence of global warming. The DTR spatial distribution and future projections of spatiotemporal variations in the case of global warming levels at 1.5 and 2.0°C under the RCP4.5 emission scenario was investigated using datasets from the Climatic Research Unit (CRU‐TS v.3.24.01) and the Coupled Model Intercomparison Project Phase 5 (CMIP5) in the Mongolian Plateau. And the main factors influencing DTR spatiotemporal variations were also evaluated using the Geo‐detector model at different time scales. The results showed that variations of the DTR significant decrease in snow‐winter dry‐warm summer climate region are higher than in other climate regions in spatial scale. As a result of the increasing rates of T min , which was more than double that of T max , the DTR decreased significantly on a temporal scale. In climate projections, the DTR was lower at global warming of 2.0°C (2037–2056) than 1.5°C (2017–2036). Precipitation was determined to be the predominant factor underlying the annual and seasonal (spring, summer, and autumn) DTR changes. Total cloud cover was the main factor in winter. Soil moisture was the key factor for the spatial variations in DTR in the warm and dry seasons. This study enhances the understanding of climate change in the Mongolian Plateau and provides a strong reference for other DTR variation studies in similar climates.