Interactions between atmospheric chemical compounds and climate have a great impact on the earth system and atmospheric chemistry. However, the online two-way chemistry-climate coupled model, an indispensable tool for quantifying chemistry-climate interactions and projecting future air quality with climate change, remains sparse due to the considerable challenge in model complexity and computational resources. We present the development and evaluation of BCC-GEOS-Chem v2.0, which couples the GEOS-Chem chemical transport model (v14.0.1) with the Beijing Climate Centre Earth System Model (BCC-ESM). Based on the modular framework of BCC-GEOS-Chem v1.0, BCC-GEOS-Chem v2.0 further couples the Harmonized Emissions Component (HEMCO) to manage anthropogenic emission inventories and natural emissions, updates the chemical mechanism, includes the feedback of aerosols and greenhouse gases, and develops the capability for high-resolution simulation. The standard chemical mechanism in the BCC-GEOS-Chem v2.0 features a comprehensive Ox-NOx-VOC-halogen-aerosol chemical scheme for the troposphere and the stratosphere. We further evaluate the performance of the BCC-GEOS-Chem v2.0 simulation in representing atmospheric chemistry and compare with the model outputs from the BCC-GEOS-Chem v1.0 and BCC-AGCM-Chem over the simulated time period (2012-2014) at a spatial resolution of T42L26 (approximately 2.8 degrees & times;2.8 degrees and 26 vertical layers with a top at 2.914 hPa). BCC-GEOS-Chem v2.0 accurately depicts the primary seasonal and spatial distributions of tropospheric ozone observed by multiple instruments, showing small global mean biases of -2.1-1.8 ppbv for mid-tropospheric (700-400 hPa) ozone concentrations relative to satellite observations, along with a high spatial correlation coefficient (r) of 0.77-0.92 for individual seasons. It also demonstrates improved performance in simulating tropospheric carbon monoxide (CO), nitrogen dioxide (NO2), formaldehyde (CH2O) and surface PM2.5 compared to both BCC-GEOS-Chem v1.0 and the BCC-AGCM-Chem. The diagnostics of tropospheric ozone budgets (a global tropospheric ozone burden of 355 Tg) and OH concentrations (0.97 & times;106 molec.cm-3) are generally consistent with observation-constrained estimates and multi-model assessment. With the inclusions of aerosol-radiation and aerosol-cloud interactions, BCC-GEOS-Chem v2.0 reproduces the expected impacts of aerosols on radiative and cloud properties, e.g., decreasing shortwave downward solar radiation and outgoing longwave radiation, increasing cloud liquid water, and suppressing precipitation. The high-resolution simulation at T159L72 (approximately 0.75 degrees & times;0.75 degrees and 72 vertical layers with a top at 0.01 hPa) further improves the model capability in resolving the fine-scale plume transport dynamics and the pollution hotspot of NO2 and PM2.5, as well as the low ozone concentration in high-NOx environment in wintertime China. The development of the BCC-GEOS-Chem v2.0 model provides a powerful tool to study climate-chemistry interactions and for future projection of global atmospheric chemistry and regional air quality.

As an important approach to improving prediction accuracy,the post-process error correction of climate model products plays an indispensable role in global operational climate systems.To enhance the prediction precision of numerical climate prediction models,this study applies the Convolutional Neural Network(CNN)approach to conduct post-process correction on key operational prediction products of the third-generation climate operational prediction system of the China Meteorological Administration(CMA),i.e.,CMA-CPSv3.The targeted products include monthly 2 m air temperature,precipitation over China,and the El Niño-Southern Oscillation(ENSO)index during the period 2001-2023.Using reanalysis data from the National Centers for Environmental Prediction(NCEP)as the observational benchmark,a dedicated correction model has been developed through deep learning training of a multi-layer CNN architecture.After model construction,changes in the model performance before and after correction are evaluated during an independent test period.Results indicate that the CNN model significantly improves the prediction accuracy of climate model products.For temperature and precipitation predictions in China,the correlation coefficient of 1-7 months lead predictions is increased by 0.1-0.5.Among these improvements,the Root Mean Square Error(RMSE)of temperature is decreased by 0.5-1.0℃,representing a reduction rate of 20%—30%.For precipitation,the correlation coefficient is increased by 0.1-0.2(an increase of 10%—20%),and the RMSE is decreased by 0.1-1.0 mm/d(a reduction rate of 3%—30%),with the RMSE reduction rate reaching 30%—50%in Eastern and Southeastern China.For the ENSO index,the correlation skill for forecasts with a lead time of 1-7 months is enhanced by 5%—7%,and the RMSE at a lead time of 7 months is reduced by 50%,suggesting that the model effectively addresses the issue of excessive oscillation amplitude of the ENSO index in the original CMA-CPSv3 model.Furthermore,this study explicitly identifies a limitation of the CNN model,i.e.,excessive intensity smoothing,when applied to the correction of extreme climate events,and proposes multi-dimensional directions for future optimization.It thus provides a technical solution that integrates scientific rigor and practical applicability for operational post-processing of CMA's climate models.

Models from the Coupled Model Intercomparison Project phase 6 exhibit a persistent warm bias in the Southern Ocean, which hinders their ability to accurately capture local and global climate variability. Here we demonstrate that the subsurface Indian Ocean warm bias—averaging 1.80 °C in the multi-model ensemble mean and present in all 39 models—accounts for approximately 37% of sea surface temperature warm bias in the Southern Ocean. Both inter-model regression analyses and numerical sensitivity experiments indicate that this subsurface Indian Ocean warm bias is transported southward by the Agulhas Current and eastward by the Agulhas Return Current and the Antarctic Circumpolar Current. This transport, coupled with vertical mixing and upwelling, exacerbates the sea surface temperature bias in the Southern Ocean. Our findings underscore the necessity for improved representation of Indian Ocean subsurface processes to enhance sea surface temperature predictions in the Southern Ocean and, by extension, global climate projections. Modelled subsurface Indian Ocean warm bias accounts for approximately 37% of sea surface temperature warm bias in the Southern Ocean, according to analysis of Coupled Model Intercomparison Project results.

Abstract. A continuous, 48-year measurement record, plus some earlier measurements, of baseline ozone at northern mid-latitudes are analyzed to quantify seasonal cycles and long-term changes of annual mean tropospheric ozone. Long-term changes are similar at all sites, and seasonal cycles are similar in the marine boundary layer (MBL) and in the free troposphere (FT), but with marked differences between those two environments. Over the last half of the 20th century, ozone concentrations increased by a factor of ~2, the seasonal cycle amplitude increased by nearly 50 %, and its maximum shifted to later in the year by 10 ± 13 days. The long-term increase ended early in the 21st century, followed by a slow decrease that reversed only a small fraction of the total earlier increase. In contrast, the seasonal cycle returned to near that of the preindustrial period. Simulations by six earth system models agree with the magnitude of the overall ozone increase and the increase ending early this century; however, observations indicate only a post-1950 increase, while models simulate a slower increase beginning in 1850. Consequently, the high bias of model simulations, while modest (~10 %) in recent years, was much larger (~87 %) in the 1950s. Qualitatively similar seasonal cycles and shifts are seen in the measurements and simulations, but simulations do not show the observed strong separation between MBL and FT behavior. We hypothesize that models simulate a background troposphere that is too NOx-rich, implying a lesser role than models simulate for methane in raising background ozone concentrations.

Abstract. This study provides a comprehensive description of the China Meteorological Administration Climate Prediction System version 4 (CMA-CPSv4), which is developed based on the fully coupled global climate-aerosol Beijing Climate Center Earth System Model (BCC-ESM1). It is updated from its previous version, CMA-CPSv3, which was based on the high-resolution Beijing Climate Center Climate System Model version 2 (BCC-CSM2-HR). In contrast to CMA-CPSv3, CMA-CPSv4 is capable of simulating the dynamic evolution of aerosols and their feedback on the climate system. This study aims to evaluate the reproducibility of atmospheric aerosols in CMA-CPSv4 under the forcing of observed atmospheric circulation. The 20-year simulations for the period 2001–2020 are conducted. The results show that CMA-CPSv4 reasonably captures the global spatial distribution and temporal variations in mass concentrations for five categories of dust, sea salt, sulfates, organic carbon, and black carbon, as well as aerosol optical depth (AOD). In East Asia, simulated fine-mode particulate matter PM2.5 concentrations are in good agreement with the CMIP6 multi-model ensemble mean (MME), although dust concentrations over the Taklamakan–Mongolia–North China regions are slightly underestimated, and sulfate concentrations are overestimated over the oceans. In addition, several severe dust pollution events in northern China are successfully reproduced, demonstrating the capability of CMA-CPSv4 to simulate aerosol concentrations and extreme events. The reasonable simulation of aerosol distribution is fundamental for studying aerosol-climate interactions and the impact of aerosols on numerical weather and climate prediction in our future work.

The newly developed third-generation Beijing Climate Center Climate System Model Medium Resolution (BCC-CSM3-MR) exhibits pronounced and seasonally varying sea surface temperature (SST) biases across the North Pacific, characterized by a “sandwich” pattern with warm biases east of Japan and cold biases in both the Okhotsk Sea and the subtropical North Pacific. These SST biases are closely associated with systematic errors in surface ocean currents, surface heat fluxes, and wind forcing, including a northward-shifted and weakened Kuroshio Extension, misrepresented westerlies and easterlies, and erroneous seasonal cooling and heating. Regional diagnostics reveal that cold SST biases intensify from late spring through early autumn and are subsequently transported downward, driven by underestimated stratification, excessive vertical mixing, and enhanced vertical shear of both zonal and meridional currents. Mixed-layer heat budget analyses further indicate that surface heat flux errors dominate SST bias formation in the Okhotsk Sea and the subtropical North Pacific, whereas the residual term, including the vertical processes, plays a leading role in the northern North Pacific during late autumn to spring. Sensitivity experiments using a forced ocean model provide a heuristic demonstration that biases in atmospheric forcing can contribute to surface cold signals consistent with those simulated by BCC-CSM3-MR. In particular, shortwave radiation errors produce the largest cooling, while precipitation and sea-level pressure biases have relatively minor impacts. Overall, the SST biases in BCC-CSM3-MR likely arise from a combined influence of atmospheric forcing errors and excessive oceanic vertical processes, highlighting key pathways for improving future model performance in the North Pacific.

The air-sea momentum flux is an important component in air-sea interactions. In climate models, the estimation of air-sea momentum flux mainly relies on bulk flux parameterization based on the Monin-Obukhov Similarity Theory. However, the bulk flux parameterization has significant biases under light wind conditions. To reduce these inaccuracies, this study analyzed observational data from marine stations. The results showed that the non-local effects, which are overlooked by the traditional Monin-Obukhov Similarity Theory, are a significant factor contributing to inaccuracies in the air-sea momentum flux parameterization. Based on this finding, this study proposed a modification equation associated with non-local effects to improve the parameterization of air-sea momentum flux under near-neutral conditions. Furthermore, the modification equation was extended to non-neutral conditions, enabling its integration into air-sea momentum flux parameterization schemes and climate models. To evaluate the applicability and effectiveness of this modification, offline and online tests were conducted using two observational datasets and the Community Atmosphere Model version 6. Assessed by the comprehensive evaluation metric DISO, it was demonstrated that the non-local effects modification evidently enhanced the calculation accuracy of air-sea momentum flux and significantly improves the simulation performance in climate models.

Including sophisticated aerosol schemes in the models of the sixth Coupled Model Inter-comparison Project (CMIP6) has not improved historical climate simulations. In particular, the models underestimate the surface air temperature anomaly (SATa) when anthropogenic sulfur emissions increased in 1960-1990, making the reliability of the CMIP6 projections questionable. This cooling bias is largely attributable to the unreasonable simulated atmospheric sulfate burden changes. Sulfate burden anomaly are closely linked to both sulfate and SO2 deposition processes. Intensified sulfate deposition directly reduces atmospheric sulfate loading, while enhanced SO2 deposition limits precursor availability for sulfate formation by oxidation. These deposition processes regulate sulfate concentrations directly and indirectly. The systematically underestimated sulfate turnover time in CMIP6 models suggests that refining SO2 deposition process rather than sulfate deposition would be a more scientific approach for model improvement. This is supported by two post-CMIP6 models that show better SATa reproduction after improving the SO2 deposition parameterizations. Strong correlations between sulfate burden anomaly and SATa persist before, during, and after the 1960-1990 period. Such temporal consistency confirms the dominant role of sulfate-related physical processes across all examined time intervals.

The representation of ice cloud microphysical processes remains a major source of uncertainty in general circulation models (GCMs). Many models assume a shape parameter (mu) of zero for the ice particle size distribution, which deviates from observations. Previous work implemented a diagnostic mu scheme in the Beijing Climate Center Atmospheric General Circulation Model (BCC-AGCM). This study investigates whether computationally cheaper fixed-mu schemes (mu = 3, 5, and 8) can achieve performance comparable to the diagnostic scheme. Results show that all modified schemes (mu > 0) reduce model biases compared with the default scheme (mu = 0), including the underestimation of cloud fraction and cloud radiative forcing, and the overestimation of total precipitation. Evaluations based on the Kling-Gupta Efficiency (KGE) and a mean rank (M-R) score indicate that the mu = 3 and mu = 5 schemes are effective alternatives (M-R = 0.486), performing most closely to the diagnostic scheme (M-R = 0.600). Similar to the diagnostic scheme, increasing mu narrows the ice particle spectrum, weakens the autoconversion of ice to snow and ice sedimentation, and enhances depositional growth. These changes increase cloud ice mass concentration in the mu = 3 and mu = 5 schemes, correcting the underestimation of high cloud fraction. In contrast, the mu = 8 scheme causes overestimation due to excessive ice mass. Consequently, the mu = 3 scheme performs best in simulating total cloud fraction and longwave cloud radiative forcing, while the mu = 5 scheme provides improvements across all variables. The results confirm that optimizing the ice crystal spectral shape parameter effectively improves model performance.

This study assesses the performance of the third generation operational climate prediction system developed by the China Meteorological Administration (CMA-CPSv3) in predicting the Asian summer monsoon on seasonal time scales. The evaluation is carried out using a 20-year set of ensemble hindcast data, which provides a solid foundation for comprehensively examining the model’s predictive capability and reliability. Results from the assessment demonstrate that CMA-CPSv3 has higher predictive skill for key components of the Asian summer monsoon system, covering a wide range of crucial climatic elements. Specifically, the model performs well in predicting the location of the summer rain belt, maximum rainfall intensity and distribution, large-scale atmospheric circulation patterns, the monsoon onset progression, as well as the interannual variability of dynamic summer monsoon indices that reflect the intensity and fluctuation of the monsoon system.Notably, the model can realistically capture the interannual variability of the western North Pacific subtropical high, a pivotal atmospheric circulation system closely associated with the position and movement of the summer rain belt over eastern China. Accurate representation of this variability lays a solid foundation for improving regional rainfall predictions. When compared with its previous version, CMA-CPSv2, the upgraded CMA-CPSv3 exhibits substantial and widespread improvements in summer precipitation prediction across the Asian continent, with particularly remarkable enhancements over eastern China, a region deeply affected by the Asian summer monsoon. Further analysis suggests that these improvements are mainly attributed to the optimized simulations of sea surface temperatures in the tropical Pacific Ocean and Indian Ocean, as well as the strengthened and more realistic ocean–atmosphere coupling processes linked to these tropical sea areas. The refined air-sea interactions enable the model to better depict the remote impacts of tropical oceans on the Asian summer monsoon system, thus elevating the overall accuracy and stability of seasonal climate predictions.

Achieving the 2 °C climate target requires the coordination of strategies for greenhouse gases (GHGs) and air pollutants mitigation, yet their complex interactions remain insufficiently explored. BCC-ESM1 Earth system model is employed to compare global climate responses under the novel SSP2-com scenario, in which both GHGs and aerosols undergo reduction, with that under the SSP2-4.5 scenario. Moreover, the relative contributions of carbon dioxide (CO2), sulfur dioxide (SO2), and black carbon (BC) to future temperature increases are analyzed. Results reveal that compared with the SSP2-4.5 scenario, the SSP2-com scenario can stabilize the end-21st-century temperature rise well below 2 °C, primarily driven by the reduction of anthropogenic CO2 emissions. A mid-term warming rebound between 2061 and 2080 is observed due to reduced aerosol cooling. SO2 reductions result in a weakening aerosol-induced radiative forcing, driving regional warming asymmetries—particularly in northern high latitudes (up to +1.5 °C in winter). Compared to CO2-only mitigation, experiments involving SO2 reductions also exhibit stronger global precipitation increases, suggesting an acceleration of the hydrological cycle under lower aerosol loading. Energy budget analysis further indicates that SO2 mitigation results in an increase in net shortwave radiation at the top of the atmosphere by approximately 0.23 W/m2 during the mid-term (2061–2080), and consequently leads to an accumulated surface energy gain of about 0.15 W/m2. These findings highlight a key trade-off: aerosol mitigation may induce mid-term warming, but remains essential for achieving air quality and climate goals. This work underscores the necessity of balancing mid-term climate–air quality trade-offs with long-term decarbonization, offering actionable insights for policymakers to design integrated pathways align with the Paris Agreement.

Based on the hindcast data from the operational climate prediction model of the China Meteorological Administration (CMA), the correction effects of precipitation prediction biases over the Tibetan Plateau during summer, particularly in July from 2006 to 2020, were systematically evaluated using the non-parametric percentile mapping (CDF) and Kalman filter-type adaptive (KEM) bias correction methods. The results indicate that the CDF method performs better in correcting systematic biases, while the KEM method shows more significant improvements in spatial correlation and anomalous trends. On the seasonal scale, the CDF method effectively reduces the overall systematic precipitation bias in the plateau region, especially decreasing bias by 80 % in the south, with an overall spatial correlation ranging between 0.72 and 0.79, while the KEM method mainly reduces precipitation bias in the central plateau by 60 % to 80 %, achieving spatial correlations above 0.8 in six years. On the subseasonal scale, both bias correction methods exhibit effects on bias and spatial correlation similar to those observed on the seasonal scale, resulting in a modest improvement in the ability to discriminate precipitation events, with an increase of 0.01 in the area under the ROC curve (AROC). The KEM method effectively enhances the prediction capability for precipitation anomaly trends by increasing the overall PS score by 6.34, reaching 84.59. Particularly for precipitation prediction correction at varying thresholds in July, the KEM method yields results that more closely align with observations, demonstrating optimal performance for moderate to heavy precipitation. In summary, CDF and KEM are functionally complementary: CDF removes pointwise systematic bias and standardizes amplitude distributions, and applying KEM subsequently to the CDF-corrected fields restores spatial-phase coherence, refines the spatial structure of anomaly trends, and suppresses low-end outliers. Combining the two methods in a "CDF first, then KEM" sequence and integrating outputs via performance-driven weighted fusion leverages their respective strengths without requiring additional observations, enabling more targeted improvement of precipitation anomaly-trend correction across sub-seasonal to seasonal transition scales over complex plateau terrain.

Ensemble prediction has been an important tool for weather forecasting, sub-seasonal to seasonal prediction, seasonal prediction, interannual prediction and even simulation of climate change, which has garnered widespread attention in the field of meteorology. This paper introduces the ensemble prediction scheme of China Meteorological Administration Climate Prediction System version 3 (CMA-CPSv3). In this scheme, we adopt the approach of combining stochastic perturbations of physical process tendencies in the atmosphere and the air-sea flux with time-lagged initial value perturbation. Based upon the version 2 of High-Resolution Beijing Climate Centre Climate System Model (BCC-CSM2-HR), we have developed a multi-layer random perturbation ensemble prediction system with relatively good ensemble sample dispersion, stability, and reliability. Results of evaluation for hindcasts over the past 20 years show that this ensemble prediction system significantly improves the prediction of precipitation and 2 m air temperature over China, as well as the El Niu00F1o-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD) and Asian Monsoon. In particular, the random perturbation of air-sea flux shows a positive effect on improving the prediction skills of ENSO and Southeast Asian Monsoon (SEAM) and Western North Pacific Summer Monsoon (WNPSM) indices. This study offers useful insights for further characterizing uncertainty in other component models of the climate system.

This study isolates the role of the 2019 Southern Hemisphere (SH) sudden stratospheric warming (SSW) for surface climate in eight Stratospheric Nudging And Predictable Surface Impacts project (SNAPSI) models. The novel nudging experiments allow for a clearer disentanglement of the SSW’s impact on surface climate than the standard free-running forecasts, in which the entire atmospheric system evolves freely and deviates from observed reality. SNAPSI models capture the downward propagation of the negative Southern Annular Mode (SAM) from the stratosphere when the zonally symmetric stratospheric circulation is nudged toward the observations. In addition, zonally asymmetric stratospheric variations amplify warm anomalies and dry conditions over eastern Australia, enhancing the Australian local high-pressure system. The composite analysis reveals that the zonally asymmetric stratospheric variations driven by the westward-shifted vortex influence the eastern Australian precipitation forecast by strengthening the downward coupling of the SAM and enhancing the meridional ridge-trough structure over Australia. Westward-shifted vortex events are identified in over 60% more ensemble members in the nudged run than in the free run, associated with precipitation closer to observed values in the nudged run. The wildfire weather potential risk in Australia is assessed using the Fraction Attributable Risk (FAR) of the Hot Dry Windy (HDW) wildfire weather index. The positive FAR of the HDW wildfire weather index indicates that the nudged stratosphere contributes up to ~30% to the increased wildfire weather risk along eastern and southern Australia, highlighting the contribution of the 2019 SSW event to Australian extreme weather from mid-October to mid-November.

Achieving the 2 u2103 climate target requires the coordination of strategies for greenhouse gases (GHGs) and air pollutants mitigation, yet their complex interactions remain insufficiently explored. BCC-ESM1 Earth system model is employed to compare global climate responses under the novel SSP2-com scenario, in which both GHGs and aerosols undergo reduction, with that under the SSP2-4.5 scenario. Moreover, the relative contributions of carbon dioxide (CO2), sulfur dioxide (SO2), and black carbon (BC) to future temperature increases are analyzed. Results reveal that compared with the SSP2-4.5 scenario, the SSP2-com scenario can stabilize the end-21st-century temperature rise well below 2 u2103, primarily driven by the reduction of anthropogenic CO2 emissions. A mid-term warming rebound between 2061 and 2080 is observed due to reduced aerosol cooling. SO2 reductions result in a weakening aerosol-induced radiative forcing, driving regional warming asymmetriesu2015particularly in northern high latitudes (up to +1.5 u2103 in winter). Compared to CO2-only mitigation, experiments involving SO2 reductions also exhibit stronger global precipitation increases, suggesting an acceleration of the hydrological cycle under lower aerosol loading. Energy budget analysis further indicates that SO2 mitigation results in an increase in net shortwave radiation at the top of the atmosphere by approximately 0.23 W/m2 during the mid-term (2061u20152080), and consequently leads to an accumulated surface energy gain of about 0.15 W/m2. These findings highlight a key trade-off: aerosol mitigation may induce mid-term warming, but remains essential for achieving air quality and climate goals. This work underscores the necessity of balancing mid-term climateu2015air quality trade-offs with long-term decarbonization, offering actionable insights for policymakers to design integrated pathways align with the Paris Agreement.

The representation of cloud microphysical processes in climate models continues to be a major challenge leading to uncertainty in climate simulations. The shape parameter (equivalent to relative dispersion) of gamma distribution for ice particles is assumed to be 0 in the Beijing Climate Center Atmospheric General Circulation Model (BCC-AGCM). This study diagnoses the shape parameter by linking it to the ice volume-mean diameter and analyzes the impact of the modified scheme on the performance of climate simulations. Results show that the modified scheme performs better in simulating global cloud fraction, cloud radiative forcing, and total precipitation compared to the control configuration, thereby significantly reducing simulation biases. The underlying physical mechanisms are driven by three key factors. First, the shape parameter in the modified scheme is greater than zero, narrowing the ice particle size distribution. This reduces the autoconversion of ice to snow and sedimentation processes while enhancing deposition growth, resulting in an increase in upper-level ice clouds. The increase in ice-clouds increases upper atmospheric temperatures, enhances atmospheric stability, and promotes the formation of lower-level clouds. Second, the improvement in cloud fraction significantly mitigates the underestimation of longwave and shortwave cloud radiative forcing. Additionally, the overestimation of precipitation is improved, including both convective and large-scale precipitation, particularly from an annual mean perspective. Increased atmospheric stability reduces convective precipitation, while weakened snow sources and enhanced sinks to reduce large-scale precipitation. The study emphasizes the importance of ice particle spectral relative dispersion and provides valuable insights for improving cloud microphysics parameterization schemes.

Human activities have greatly altered Earth’s radiative balance, necessitating assessments that couple natural and social systems to address the resulting impacts and interactions. However, traditional Earth System Models (ESMs) and integrated assessment models often split to simulate changes in the two systems and neglect the feedback between these systems, limiting our understanding of the pathways to carbon neutrality, the associated global warming level and climate change impacts. This study constructs a coupled natural-social systems framework to bridge this gap, integrating the China-in-global energy model with the Beijing Climate Center ESM version 1. The results show that bidirectional feedbacks in the natural-social system increase the demand for electricity under China’s carbon neutrality target, increasing the pressure to reduce emissions and driving up the carbon prices. Meanwhile, carbon neutrality does not eliminate all negative climate impacts and can substantially reduce the economic output of climate-vulnerable sectors in China, highlighting the need for early adaptation measures. This study emphasizes the importance of planning China’s climate neutrality pathways from a coupled natural-social system perspective.

Integration of weather and climate forecasting is currently the frontier of numerical modeling development in China, and dynamic downscaling allows for improving the performance and resolution of global climate models to the weather scale. Focusing on the “23.7” extreme rainstorm (July 29, 00:00 - August 2, 00:00 UTC) in the Beijing-Tianjin-Hebei region (BTH), this study assesses predictions from the China Meteorological Administration Climate Prediction System version 3 (CMA-CPSv3, 45 km resolution) and 9-km dynamic downscaling hindcasts from the Weather Research and Forecasting model (WRF-9km). Unlike traditional climate anomalies approaches, direct outputs are used for evaluation, similar to weather forecasting tests. By examining, both the CMA-CPSv3 forecasts and the WRF-9km hindcasts offer a 5-day prediction window for this rainstorm. They successfully predict the rainstorms and related atmospheric circulations from July 24th onward, aligning with observed and reanalyzed data. WRF-9km, with the higher resolution and optimized physical processes, outperforms CMA-CPSv3, particularly in precipitation spatial distribution and center intensity. The WRF-9km 7/24 hindcast exhibits the most significant enhancement compared to the corresponding CMA-CPSv3 forecast. This improvement is notably reflected in the substantial increase in spatial correlation, rising from 0.68 to 0.79, as well as a reduction in the difference of center values, decreasing from -51% to -20%. Furthermore, the WRF-9km 7/24 hindcast also improves the Critical Success Index by 0.08, the Success Rate by 0.08, and the Probability of Detection by 0.29 for heavy rainfall (over 25.0 mm/d). However, improvements in large-scale circulations with WRF-9km are limited, which may restrict advancements in predictability. In conclusion, the WRF-9km can enhance the performance and resolution of CMA-CPSv3 predictions, which can serve as one route for CMA-CPSv3 to achieve weather-climate integration.

Belg is the primary rainy season in southern Ethiopia and the secondary rainy season in the remaining regions. The major water supply for Belg is from the Southwest Indian Ocean, leading to the strong rainfall over southwestern and southern Ethiopia. This study investigates the spatiotemporal variability of Belg over the common period 1985–2023 across Ethiopia. The first and second typical anomalous modes of Belg account for 41.9