CMIP5 Models Research Articles

Evaluation of 22 CMIP6 model-derived global soil moisture products of different shared socioeconomic pathways

The Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) provides ample soil moisture (SM) products during 2015–2100, which are widely used in investigating future hydrological pattern. However, there remains limited understanding regarding the accuracy of multi-model CMIP6 SM future simulations following different Shared Socioeconomic Pathways (SSP). This study assesses the spatial–temporal accuracy of 22 CMIP6 model-derived SM products using 670 ground observation stations globally during 2015–2023. Each CMIP6 model encompasses SM estimations from SSP1-2.6 (The sustainable pathway), SSP2-4.5 (The medium pathway), and SSP5-8.5 (Fossil-fueled development). The latest Soil Moisture Active Passive (SMAP) and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land (ERA5-Land) SM products serve as reference benchmarks. The SM among three SSPs within the same model exhibits similar performance in in situ validation, the extended triple collocation assessment, and the classical seasonal decomposition trend analysis. This similarity may suggest a subtle variation in the influence of different SSPs on SM in the early stages of the future scenario. The majority of CMIP6 model-derived SM could reasonably capture the spatial distribution pattern and temporal evolution trend of the truth value. Meanwhile, noticeable discrepancies in accuracy, particularly regarding goodness-of-fit, are evident when compared to SMAP and ERA5-Land SM products. The CMIP6 models could provide better SM estimation in temperate and tropical zones than cold zones. In terms of differences between the land cover types, CMIP6 model-derived SM products perform well over various vegetated regions except for barren, where there are sequential overestimation and relatively low fitting degree. Extensive accuracy discrepancies exist among the 22 CMIP6 model-derived SM products due to variations in model parameterization methods and atmospheric variables.

The Phase Shift between the Global Surface Temperature and the CO2 Content in the Atmosphere According to Simulations with the Ensemble of CMIP6 Models

The Phase Shift between the Global Surface Temperature and the CO2 Content in the Atmosphere According to Simulations with the Ensemble of CMIP6 Models

Evaluation and projection of extreme precipitation using CMIP6 model simulations in the Yellow River Basin

Abstract The capabilities of 23 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6 were evaluated for six extreme precipitation indices from 1961 to 2010 using interannual variability and Taylor skill scores in the Yellow River Basin and its eight subregions. The temporal variations and spatial distributions of extreme precipitation indices were projected from 2021 to 2050 under the shared socioeconomic pathway scenarios (SSP2–4.5 and SSP5–8.5). The results show that most GCMs perform well in simulating extreme values (1-day maximum precipitation (RX1day) and 5-day maximum precipitation (RX5day)), duration (consecutive dry days), and intensity index (simple daily intensity index (SDII)), and perform poor in simulating the threshold indices (precipitation on very wet days (R95p) and number of heavy precipitation days (R10mm)). The projected changes in extreme precipitation indicate that under the SSP2-4.5 scenario, future extreme precipitation will increase by 15.7% (RX1day), 15.8% (RX5day), 30.3% (R95p), 1d (R10mm), and 6.6% (SDII), respectively, decrease by 2.1d (CDD). The aforementioned changes are further enhanced under the SSP5-8.5 scenario. Extreme precipitation changes widely in Hekou Town to Longmen, in the northeastern part of the region from Longmen to Sanmenxia, below Huayuankou, and in the interflow basin.

Assessment of the Madden‐Julian Oscillation in CMIP6 Models Based on Moisture Mode Theory

AbstractThe moist processes of the Madden‐Julian Oscillation (MJO) in the Coupled Model Intercomparison Project Phase 6 models are assessed using moisture mode theory‐based diagnostics over the Indian Ocean (10°S–10°N, 75°E–100°E). Results show that no model can capture all the moisture mode properties relative to the reanalysis. Most models satisfy weak temperature gradient balance but have unrealistically fast MJO propagation and a lower moisture‐precipitation correlation. Models that satisfy the most moisture mode criteria reliably simulate a stronger MJO. The background moist static energy (MSE) and low‐level zonal winds are more realistic in the models that satisfy the most criteria. The MSE budget associated with the MJO is also well‐represented in the good models. Capturing the MJO's moisture mode properties over the Indian Ocean is associated with a more realistic representation of the MJO and thus can be employed to diagnose MJO performance.

The prediction of precipitation changes in the Aji-Chay watershed using CMIP6 models and the wavelet neural network

ABSTRACT Greenhouse gases affect climate system disturbances. This research employs sixth generation CMIP6 models in the SSP5.85 scenario and extends the use of the neural wavelet network to predict precipitation variations for the future (2025–2065). Kendall's trend test is used to assess changes in precipitation trends for observed and projected periods. An analysis of variance (ANOVA) validates models under SSP5.85 by comparing observed precipitation with model predictions. A multi-layer perceptron neural network assesses climate change's impact on future precipitation. Findings indicate future precipitation is projected to fluctuate from −0.146 to over −2.127 mm compared to the baseline period. The observed period showed a significant 3.37% monthly precipitation decrease within the watershed. The CanESM5 model predicts a 3.916 reduction in precipitation with 95% confidence, while INM-CM4-8 and MRI-ESM2-0 models are less certain. The minor difference between CanESM5's predicted (−5.91) and observed (−5.05) precipitation suggests a slight variance. On the other hand, the wavelet neural network (WNN) model predicts that precipitation in this region will increase in the future. In general, this study predicts a decrease in precipitation for the Aji-Chay watershed in Iran over the next decade, could lead to serious issues like lower crop yields, rising food prices, and even droughts.

Root proliferation adaptation strategy improved maize productivity in the US Great Plains: Insights from crop simulation model under future climate change

Adaptation measures are essential for reducing the impact of future climate risks on agricultural production systems. The present study focuses on implementing an adaptation strategy to mitigate the impact of future climate change on rainfed maize production in the Eastern Kansas River Basin (EKSRB), an important rainfed maize-producing region in the US Great Plains, which faces potential challenges of future climate risks due to a significant east-to-west aridity gradient. We used a calibrated CERES-Maize crop model to evaluate the impacts of baseline climate conditions (1985–2014), late-term future climate scenarios (under the SSP245 emission pathway and CMIP6 models), and a novel root proliferation adaptation strategy on regional maize yield and rainfall productivity. Changes in the plant root system by increasing the root density could lead to yield benefits, especially under drought conditions. Therefore, we modified the governing equation of soil root growth in the CERES-Maize model to reflect the genetic influence of a maize cultivar to improve root density by proliferation. Under baseline conditions, maize yield values ranged from 6522 to 12,849 kgha−1, with a regional average value of 9270 kgha−1. Projections for the late-term scenario indicate a substantial decline in maize yield (36 % to 50 %) and rainfall productivity (25 % to 42 %). Introducing a hypothetical maize cultivar by employing root proliferation as an adaptation strategy resulted in a 27 % increase in regional maize yield, and a 28 % increase in rainfall productivity compared to the reference cultivar without adaptation. We observed an indication of spatial dependency of maize yield and rainfall productivity on the regional precipitation gradient, with counties towards the east having an implicit advantage over those in the west. These findings offer valuable insights for the US Great Plains maize growers and breeders, guiding strategic decisions to adapt rainfed maize production to the region's impending challenges posed by climate change.

G6-1.5K-SAI: a new Geoengineering Model Intercomparison Project (GeoMIP) experiment integrating recent advances in solar radiation modification studies

Abstract. The Geoengineering Model Intercomparison Project (GeoMIP) has proposed multiple model experiments during phases 5 and 6 of the Climate Model Intercomparison Project (CMIP), with the latest set of model experiments proposed in 2015. With phase 7 of CMIP in preparation and with multiple efforts ongoing to better explore the potential space of outcomes for different solar radiation modifications (SRMs) both in terms of deployment strategies and scenarios and in terms of potential impacts, the GeoMIP community has identified the need to propose and conduct a new experiment that could serve as a bridge between past iterations and future CMIP7 experiments. Here we report the details of such a proposed experiment, named G6-1.5K-SAI, to be conducted with the current generation of scenarios and models from CMIP6 and clarify the reasoning behind many of the new choices introduced. Namely, compared to the CMIP6 GeoMIP scenario G6sulfur, we decided on (1) an intermediate emission scenario as a baseline (the Shared Socioeconomic Pathway 2-4.5), (2) a start date set in the future that includes both considerations for the likelihood of exceeding 1.5 °C above preindustrial levels and some considerations for a likely start date for an SRM implementation, and (3) a deployment strategy for stratospheric aerosol injection that does not inject in the tropical pipe in order to obtain a more latitudinally uniform aerosol distribution. We also offer more details regarding the preferred experiment length and number of ensemble members and include potential options for second-tier experiments that some modeling groups might want to run. The specifics of the proposed experiment will further allow for a more direct comparison between results obtained from CMIP6 models and those obtained from future scenarios for CMIP7.

Effects of climate change on streamflow in the Dez Basin of Iran using the IHACRES model based on the CMIP6 model

Abstract Understanding the changes in river flow is an important prerequisite for designing hydraulic structures as well as managing surface water resources in basins. By using the LARS-WG statistical downscaling model, the outputs of the general circulation model of the sixth report, including the ACCESS-ESM1 and BCC-CSM-MR models, under the SSP5.8.5 and SSP2.4.5 release scenarios. A more accurate spatial scale and daily precipitation and temperature time series were obtained for the studied area during the period of 2015–2043. Then the IHACRES rainfall-runoff model was calibrated in the study area. Based on the fit statistics in the calibration and validation stages, the overall performance of the developed model was evaluated as satisfactory. The calibrated hydrological model was driven by rainfall data and reduced air temperature to predict the effect of climate change on the output of the studied basin. The study showed that the studied basin has more rainfall (on average, 20.8% in the ACCESS-ESM1 model and 33.2% in the BCC-CSM2-MR model). The flow rate of the main river in the ACCESS-ESM1 model will decrease by 15% compared to the base period, and in the BCC-CSM2-MR model, it will increase by 16% compared to the base period.

Changes in Drought Characteristics in the Yellow River Basin during the Carbon-Neutral Period under Low-Emission Scenarios

Droughts have a severe impact on the environment and social economy, and predicting their future changes is challenging due to significant uncertainties in climate change and human activities. Many countries have pledged to achieve carbon neutrality to limit global warming; however, few studies have focused on drought changes during the carbon-neutral period. Here, we analyzed the variations in drought characteristics across the Yellow River Basin (YRB) during the carbon-neutral period under two low-emission scenarios from 7 CMIP6 model outputs. The results show that the temperature and precipitation will increase significantly during the 2015–2100 period under both SSP1-1.9 and SSP1-2.6 scenarios. Compared to the historical period (1979–2014), the hydrological drought frequency is projected to decrease by 15.5% (13.0–18.1%), while drought severity is expected to increase by 14.4% (13.2–15.7%) during the carbon-neutral period. Meteorological droughts exhibit a similar changing trend, although the results vary between different regions. In general, more severe hydrological droughts may occur in the southern YRB in the carbon-neutral period under low-emission scenarios. This study has implications for future drought mitigation within the Yellow River Basin.

Development of an assembled model for the projection of climate change in the Endorheic Basin of the Bolivian Altiplano: Evaluation and validation of historical temperature simulations of CMIP6 models

Development of an assembled model for the projection of climate change in the Endorheic Basin of the Bolivian Altiplano: Evaluation and validation of historical temperature simulations of CMIP6 models

Increase in the variability of terrestrial carbon uptake in response to enhanced future ENSO modulation

El Niño–Southern Oscillation (ENSO) is a major driver of climate change in middle and low latitudes and thus strongly influences the terrestrial carbon cycle through land–air interaction. Both the ENSO modulation and carbon flux variability are projected to increase in the future, but their connection still needs further investigation. To investigate the impact of future ENSO modulation on carbon flux variability, this study used 10 CMIP6 earth system models to analyze ENSO modulation and carbon flux variability in middle and low latitudes, and their relationship, under different scenarios simulated by CMIP6 models. The results show a high consistency in the simulations, with both ENSO modulation and carbon flux variability showing an increasing trend in the future. The higher the emissions scenario, especially SSP5-8.5 compared to SSP2-4.5, the greater the increase in variability. Carbon flux variability in the middle and low latitudes under SSP2-4.5 increases by 30.9% compared to historical levels during 1951–2000, while under SSP5-8.5 it increases by 58.2%. Further analysis suggests that ENSO influences mid- and low-latitude carbon flux variability primarily through temperature. This occurrence may potentially be attributed to the increased responsiveness of gross primary productivity towards regional temperature fluctuations, combined with the intensified influence of ENSO on land surface temperatures.摘要ENSO是中低纬度地区气候系统的主要驱动因素, 对陆地碳循环有重要影响. 本研究基于10个CMIP6地球系统模式, 分析了不同情景下ENSO变率与中低纬度地区总初级生产力变率的关系. 结果显示, 未来ENSO变率和总初级生产力变率在未来多数模式均显示为增加. 在未来情境下(2051-2100年), 中低纬度地区的总初级生产力变率较历史时期(1951–2000年)增加了30.9%(SSP2-4.5), 58.2%(SSP5-8.5). 进一步分析表明, ENSO主要通过温度影响中低纬度碳通量变率. 这种现象可能归因于总初级生产力对温度的响应增强, 以及ENSO对陆地表面温度的影响.

Assessment of the Global Relationship of Different Types of Droughts in Model Simulations Under High Anthropogenic Emissions

AbstractThis study provides a global analysis of the relationship between drought metrics obtained from several climatic, hydrologic and ecological variables in a climate change framework using CMIP6 model data. A comprehensive analysis of the evolution of drought severity on a global scale is carried out for the historical experiment (1850–2014) and for future simulations under a high emissions scenario (SSP5‐8.5). This study focuses on comparing trends in the magnitude and duration of drought events according to different standardized indices over the world land‐surface area. The spatial and temporal relationship between the different drought indices on a global scale was also evaluated. Overall, there is a fairly large consensus among models and drought metrics in pointing to drought increase in southern North America, Central America, the Amazon region, the Mediterranean, southern Africa and southern Australia. Our results show important spatial differences in drought projections, which are highly dependent on the drought metric employed. While a strong relationship between climatic indices was evident, climatic and ecological drought metrics showed less dependency over both space and time. Importantly, our study demonstrates uncertainties in future projections of drought trends and their interannual variability related to the relationship among indices, stressing the importance of coherent climatic, hydrological and plant physiological patterns when analyzing CMIP6 model simulations of droughts under a warming climate scenario.

Are our lands getting wetter or drier? A perspective from Climate scenario data in Upper Brantas

Changes in rainfall are considered influential for sustaining the land resources in tropical agriculture. Many areas in East Java are classified as top priorities in adaptation and mitigation programs, especially in the agricultural and water resources sectors. Climate change impacts have been documented through global-scale General Circular Models (GCM). This study examined the future estimated climate generated from the CMIP5 model of the globe’s climate of four greenhouse gas scenarios in the Brantas Hulu watershed and studied the consequences for the agricultural climate suitability. The study exploited the 18 climate calculation models at the regional level by focusing on RCP 4.5, the development scenario considered in Indonesia’s climate action. During the 2000-2099 predictions, temperature and precipitation increased in all models. If this increase continues to be experienced without efforts for proper mitigation and adaptation plans, the disruption in agricultural products might pose a major consequence for food security.

Climate change projection using statistical downscaling model over southern coastal Iran

Iran is highly vulnerable to climate change, particularly evident in shifting precipitation and temperature patterns, especially in its southern coastal region. With these changing climate conditions, there is an urgent need for practical and adaptive management of water resources and energy supply to address the challenges posed by future climate change. Over the next two to three decades, the effects of climate change, such as precipitation and temperature, are expected to worsen, posing greater risks to water resources, agriculture, and infrastructure stability. Therefore, this study aims to evaluate the alterations in mean daily temperature (Tmean) and total daily rainfall (rrr24) utilizing climate change scenarios from both phases 5 and 6 of the Coupled Model Inter-comparison Project (CMIP5 and CMIP6, respectively) in the southern coastal regions of Iran (Hormozgan province), specifically north of the Strait of Hormuz. The predictions were generated using the Statistical Downscaling Model (SDSM) and National Centre for Environmental Prediction (NCEP) predictors, incorporating climate change scenarios from CMIP5 with Representative Concentration Pathways (RCPs) 2.6, 4.5, and 8.5 and CMIP6 with Shared Socioeconomic Pathways (SSPs) 1, 2, and 5. The analysis was conducted for three distinct time periods: the early 21st century (2021–2045), middle 21st century (2046–2071), and late 21st century (2071–2095). The results indicated that the CMIP5 model outperformed the CMIP6 model in simulating and predicting Tmean and rrr24. In addition, a significant increase in Tmean was observed across all the scenarios and time periods, with the most pronounced trend occurring in the middle and late 21st century future periods. This increase was already evident during the base period of 2021–2045 across all scenarios. Moreover, the fluctuations in precipitation throughout the region and across all scenarios were significant in the three examined future periods. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of Tmean (+1.22 °C) in Bandar Lengeh station in 2071–2095 period. The lowest change magnitude of Tmean among CMIP5 scenarios was found in RCP4.5 (−1.94 °C) in Ch station in 2046–2070 period. The results indicated that among CMIP5 scenarios, RCP8.5 had highest changes of rrr24 (+150.2 mm) in Chabahar station in 2071–2095 period. The lowest change magnitude of rrr24 among CMIP5 scenarios was found in RCP8.5 (−25.8 mm) in Bandar Abbas station in 2046–2070 period. In conclusion, the study reveals that the coastal area of Hormozgan province will experience rising temperatures and changing rainfall patterns in the future. These changes may lead to challenges such as increased water and energy consumption, heightened risks of droughts or floods, and potential damage to agriculture and infrastructure. These findings offer valuable insights for implementing local mitigation policies and strategies and adapting to emerging climate changes in Hormozgan's coastal areas. For example, utilizing water harvesting technologies, implementing watershed management practices, and adopting new irrigation systems can address challenges like water consumption, agricultural impacts, and infrastructure vulnerability. Future research should accurately assess the effect of these changes in precipitation and temperature on water resources, forest ecosystems, agriculture, and other infrastructures in the study area to implement effective management measures.

Natural Sinks and Sources of CO2 and CH4 in the Atmosphere of Russian Regions and Their Contribution to Climate Change in the 21st Century Evaluated with the CMIP6 Model Ensemble

Natural Sinks and Sources of CO2 and CH4 in the Atmosphere of Russian Regions and Their Contribution to Climate Change in the 21st Century Evaluated with the CMIP6 Model Ensemble

Multi-model ensemble of frost risks across East Asia (1850–2100)

Frost events can cause the deterioration of a wide range of heritage materials, including stone, brick and earth. In a warming world, the frequency and location of frost events is likely to change, affecting the conservation strategies required at heritage sites. We use a multi-model ensemble approach to investigate three types of frost events in East Asia: freeze–thaw cycles; deep frost days and wet frosts. The study uses nine CMIP6 models for the period 1850 to 2100, with future projections run under the SPS585 scenario. Additional analysis is undertaken for five specific 2° ✕ 2° areas located across East Asia. The three frost event parameters are spatially and temporally distinct. A decrease in all three frost parameters is found in Japan, South Korea and East China, with some areas projected to have no frost events by the end of the twenty-first century. However, Northwest China is distinctive as wet frosts are projected to increase over the twenty-first century, while on the Tibetan plateau of Southwest China, freeze–thaw cycles are projected to increase. This suggests that except in some localised regions, heritage managers can focus on risks other than frost weathering in developing plans to address climate change.Graphical

Developing climate services for vulnerable islands in the Southwest Indian Ocean: A combined statistical and dynamical CMIP6 downscaling approach for climate change assessment

Climate change is a global challenge necessitating adaptation at the local level. Small island developing states (SIDS) in the southwest Indian Ocean (SWIO) basin are particularly vulnerable and already facing significant challenges due to climate variability and extreme weather events like tropical cyclones (TCs). Tailored climate services, catering to their specific needs and contexts, are crucial for formulating appropriate adaptation strategies. This study aims to fill the gap of localized and reliable information for climate services in the SWIO region. A 12-km resolution regional climate model is dynamically downscaled from a CMIP6 model to capture the climatology of tropical cyclones. Outputs are bias-corrected at kilometer-scale resolution over multiple islands. A subset of CMIP6 simulations is also statistically downscaled over La Réunion to quantify model uncertainties at the local scale and compare statistical and dynamical downscaling methods.CMIP6 models project an average increase in daily mean temperatures over small islands ranging from 1.2 °C (SSP1-2.6) to 3.7 °C (SSP5-8.5) by the end of the century compared to the reference period of 1981–2010, and up to 4.4 °C on Madagascar (SSP5-8.5). The dry season in the SWIO region is anticipated to become significantly drier, with precipitation deficits ranging from 10 % to 40 %, primarily due to a delayed onset of the rainy season. The cyclonic risk is expected to increase due to stronger cyclone intensities, a higher proportion of strong TCs, and the poleward migration of very intense TCs by one to two degrees latitude, further amplifying the risk faced by the Mascarene islands.

Efficient inference and learning of a generative model for ENSO predictions from large multi-model datasets

Historical simulations of global sea-surface temperature (SST) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are analyzed. A state-of-the-art deep learning approach is applied to provide a unified access to the diversity of simulations in the large multi-model dataset in order to go beyond the current technological paradigm of ensemble averaging. Based on the concept of a variational auto-encoder (VAE), a generative model of global SST is proposed in combination with an inference model that aims to solve the problem of determining a joint distribution over the data generating factors. With a focus on the El Niño Southern Oscillation (ENSO), the performance of the VAE-based approach in simulating various central features of observed ENSO dynamics is demonstrated. A combination of the VAE with a forecasting model is proposed to make predictions about the distribution of global SST and the corresponding future path of the Niño index from the learned latent factors. The proposed ENSO emulator is compared with historical observations and proves particularly skillful at reproducing various aspects of observed ENSO asymmetry between the two phases of warm El Niño and cold La Niña. A relationship between ENSO asymmetry and ENSO predictability is identified in the ENSO emulator, which improves the prediction of the simulated Niño index in a number of CMIP5 models.

Northward shift of Indian summer monsoon and intensifying winter westerlies cause stronger precipitation seasonality over Pamirs and its downstream basins in the 21st century

Hydroclimate will change over Pamirs and its downstream basins (PDB), including Indus River, Tarim River, Amu Darya and Syr Darya Basins, in response to the variation of Indian summer monsoon (ISM) and mid-latitude westerlies. However, the precipitation variation and its mechanism over PDB in the 21st century are yet not fully understood. Here, the best models ensemble selected from 25 CMIP6 models under SSP2–4.5 and SSP5–8.5 scenarios is applied to detect the precipitation variations over PDB in the 21st century. A remarkable dipolar pattern is found in both summer and winter precipitation over PDB, particularly in the central Indus River Basin and upper Amu and Syr Darya Basins. The central Indus River Basin (upper Amu and Syr Darya Basins) will experience an increasingly wet (dry) summer in response to northward ISM and a dry (wet) winter driven by mid-latitude westerlies. The amplifying dipolar pattern of seasonal precipitation thus increases the water resource vulnerability over PDB and emphasizes the role of Pamirs in modulating the water resources over surrounding basins, especially the Amu Darya and Syr Darya Basins in the future. The findings underscore the need for prioritizing policies by considering the impacts of precipitation seasonality on social planning.

Decreased river runoff on the Mongolian Plateau since around 2000

ContextMongolian Plateau is one of the largest contingent arid and semi-arid regions of the world. Rivers on the plateau provide vital water for millions of indigenous Mongolian people and numerous endangered wildlife, but are increasingly disturbed by climate change and human activities. Yet, long-term changes in river runoff across the plateau remain poorly studied due to data unavailability.ObjectivesThis study aimed to analyze the temporal trends in gauged river runoff on the Mongolian Plateau, identify drivers of the observed changes, and evaluate CMIP6 models' performance in simulating historical runoff changes across the plateau.MethodsWe compiled possibly the largest database of long-term (20 - 71 years) river runoff for the plateau comprising measurements over 30 major rivers. Statistical analyses were conducted to assess trends in river runoff and correlations between runoff and climatic variables. Additionally, we applied the Budyko curve framework to identify the influence of human activities on river runoff in specific basins. Furthermore, we compared ground-measured runoff data with simulations from CMIP6 models to evaluate the ability of CMIP6 models to replicate runoff dynamics in typical arid and semi-arid regions.ResultsWe observed pervasive and abrupt reduction in runoff in 21 out of the 30 rivers within 5 years before or after the year of 2000. Variations in river runoff were most significantly caused by changes in total precipitation (TP). In particular, 27 rivers experienced abrupt TP changes around 2000, and there was a significant positive correlation between annual fluctuations in TP and river runoff for 18 rivers. In addition to climate factors, the influence of human activities was identified in certain basins. The CMIP6 simulations failed to capture the abrupt changes in runoff occurred pervasively across the plateau around 2000.ConclusionsAround 2000, major rivers on the Mongolian Plateau, especially in Inner Mongolia, experienced runoff declines, primarily due to TP changes. Human activities like dam construction and water diversion further influenced local runoff. CMIP6 historical runoff simulations were inaccurate across the plateau, highlighting the difficulty of simulating river runoff in this critical region. Our study could contribute to a more comprehensive understanding of the water resource changes on the Mongolian Plateau, with direct implications for enhancing ecological conservation and management practice.