Urban vegetation, the core component of green infrastructure and critical for sustainable cities, is profoundly affected by the process of urbanization. Urbanization not only leads to substantial vegetation loss (direct impact) but also fosters urban vegetation growth (indirect impact). However, the extent to which these direct and indirect impacts affect vegetation dynamics across cities worldwide and how urban greening will change in the future remain unclear. Using satellite-based greenness and impervious surface datasets, we show that positive indirect impacts mitigated 56.85% of the negative direct impacts across 4,718 cities worldwide from 2000 to 2019. Notably, the offsetting coefficient is much greater in Global North cities (79.13%) than in Global South cities (38.01%) partly due to their socioeconomic differences. This disparity in urban greening dynamics will continue in the future. Approximately 60% of Global North cities and 30% of Global South cities will become greener by 2040. Our results reveal the divergent trade-offs between vegetation loss and enhanced vegetation growth in cities of different socioeconomic levels and stages of urbanization. Such insights are crucial for a comprehensive understanding of urban greening dynamics and for devising strategies to attain sustainable development goals.

Abstract Land use and land cover changes (LULCCs) can influence surface temperature through local and nonlocal biophysical processes, which remain inadequately addressed. In this study, we separate the local and nonlocal effects of historical (1850–2014) LULCCs based on model outputs from the Coupled Model Intercomparison Project Phase 6. We also attempt to explore the sources of intermodel differences in the effects of LULCCs. The multimodel mean shows a cooling effect of −0.05°C (with an intermodel range of −0.24–0.06°C) at the global scale due to cropland and pastureland expansion, consisting of dominant nonlocal cooling of −0.06°C (with an intermodel range of −0.26–0.06°C) and slight local warming of 0.01°C (with an intermodel range of −0.01–0.05°C). The modeling results show some clear consistency in the effects of LULCCs despite considerable intermodel uncertainties. The local effects cause warming at low latitudes and cooling in boreal regions via changes in upward shortwave radiation and sensible and latent heat fluxes. The nonlocal effects mainly cause cooling via decreases in downward longwave radiation and increases in upward shortwave radiation. Intermodel differences in the total effects are dominated by those in the nonlocal effects, which are further attributed to divergent changes in downward longwave radiation and sensible heat flux across the models. This study highlights the importance of the nonlocal effects of LULCCs in terms of strength and intermodel uncertainty, with implications for designing land‐based solutions aimed at climate change mitigation.

The increasing frequency of European heatwaves and the associated impacts on ecosystems have raised widespread concern during the last two decades. The partitioning of surface energy between latent and sensible heat fluxes plays a pivotal role in regulating heat and water exchange between the land surface and the atmosphere. However, the responses of surface energy partitioning during heatwave events and the contributions of changes in energy partitioning to heatwave development have been underexplored. Here, we investigated the responses of surface energy exchange to temperature extremes during four devastating European heatwaves (2003, 2010, 2018, and 2022) based on long‒term observations from 31 flux towers. Our results demonstrated that the divergent responses of surface energy exchange to heatwaves were modulated by vegetation type and background climate in Europe. Forests maintained similar latent heat fluxes as the climatological mean but largely increased sensible heat under heat‒stressed conditions. While grasslands and croplands tended to increase sensible heat by suppressing latent heat during heatwaves, especially under water‒stressed conditions. Furthermore, the changes in surface energy partitioning strengthened positive land‒atmosphere feedbacks during the heatwave period, leading to unprecedented temperature extremes. This study highlights the importance of surface energy partitioning in land‒atmosphere interactions and heatwave developments.

Tibetan Plateau (TP) is named “the Asian water tower” since it provides fresh water for hundreds of millions of people in Asia. Tibetan Plateau vortices (TPVs) are the major precipitation-producing system over TP, and thus are of importance to the water cycle and the severe disasters of TP and the downstream regions. Using the Poisson regression, an empirical genesis index of TPVs GTPV is constructed based on a long-term database of TPVs derived from the reanalysis datasets. The spatiotemporal characteristics of TPVs are well reproduced by GTPV. Relative contribution of environmental factors shows that, the environmental factors reflected the sensible heat (elevation and the ground-air temperature difference) play a crucial role in the spatial distribution of TPVs, and the environmental factors represented the latent heat (the difference between upper-level and lower-level divergence, relative vorticity at 500 hPa, and the mid-tropospheric humidity) determine the interannual variations of TPVs. In the annual cycle of TPVs, both the latent heat and the sensible heat are of importance. The genesis climatology of TPVs from the Yearbook by manual tracing and GTPV via other reanalysis datasets are also used to evaluate the performance of the genesis index. It implies the potential capability of GTPV to understand the mechanism of the genesis and development of TPVs, and to analyze the TPVs from the GCMs with the coarse resolutions.

Satellite observations have shown evident vegetation greening in China during the last two decades. The biophysical effects of vegetation changes on near-surface air temperature (SAT) remain elusive because prior studies focused on the effects on land surface temperature (LST). SAT is more relevant to climate mitigation and adaptation, as this temperature is experienced by humans. Here, we provide the first observational evidence of the greening effects on SAT and SAT extremes in China during 2001–2018 using the ‘space-for-time’ method. The results show a negative SAT sensitivity to greening (–0.35 °C m ^2 m ^–2 ) over China and a cooling effect of −0.08 °C on SAT driven by vegetation greening during the study period. Such a cooling effect is stronger on high SAT extremes, particularly over arid/semiarid areas, where greening could bring an additional cooling of −0.04 °C on the hottest days. An attribution analysis suggests that the main driving factor for the cooling effect of greening is the evapotranspiration change for arid/semiarid regions and the aerodynamic resistance change for humid regions. This study reveals a considerable climate benefit of greening on SAT, which is more concerned with natural and human system health than the greening effects on LST.

Increasing the urban tree cover percentage (TCP) is widely recognized as an efficient way to mitigate the urban heat island effect. The cooling efficiency of urban trees can be either enhanced or attenuated on hotter days, depending on the physiological response of urban trees to rising ambient temperature. However, the response of urban trees’ cooling efficiency to rising urban temperature remains poorly quantified for China’s cities. In this study, we quantify the response of urban trees’ cooling efficiency to rising urban temperature at noontime [∼1330 LT (local time), LT=UTC+8] in 17 summers (June, July, and August) from 2003–19 in 70 economically developed cities of China based on satellite observations. The results show that urban trees have stronger cooling efficiency with increasing temperature, suggesting additional cooling benefits provided by urban trees on hotter days. The enhanced cooling efficiency values of urban trees range from 0.002 to 0.055°C

Abstract Tibetan Plateau (TP) snow cover is featured by sub‐seasonal changes, affecting weather and climate in surrounding and downstream areas. Previous studies emphasize the effect of background atmospheric circulation on rapid changes of TP snow cover as a whole. However, spatial discrepant changes of snow cover over the TP with complex topography and uneven snowfall remain unaddressed. Our research indicates that snow cover fraction dominates the rapid changes of surface albedo across the TP, and snow depth also significantly influences surface albedo changes through modulating snow albedo in central and eastern TP with shallow snow. However, the excessive snow amount and empirical snow cover fraction schemes introduce spatially divergent biases of surface albedo changes in simulations. Our research highlights the instant response of TP surface albedo to both snow coverage and depth in snow season, and provides a promising perspective for improving TP snow and surface albedo simulations.

China has shown a world-leading vegetation greening trend since 2000, which may exert biophysical effects on near-surface air temperature (SAT). However, such effects remain largely unknown because prior studies either focus on land surface temperature, which differs from SAT, or rely on simulations, which are limited by model uncertainties. As a widely used metric in climate and extremes research, SAT is more relevant to human health and terrestrial ecosystem functions. Therefore, it is necessary to explore impacts of greening on SAT and extremes based on observations. Here, we investigate the greening effects on SAT and subsequent extremes over 2003–2014 in China based on high-resolution SAT observations combined with satellite datasets. We find that greening can cause cooling effects on the mean SAT and more pronounced cooling effects on SAT extremes over semiarid regions. Such cooling effects are attributed to enhanced evapotranspiration caused by greening and strong coupling between evapotranspiration and SAT in semiarid regions. Semiarid regions in China are the transitional zone of both climate and ecosystem and deeply influenced by human agricultural and pastoral activities. These factors make the ecosystem of these regions fragile and extremely vulnerable to climate change. Our results reveal a considerable climate benefit of greening to natural and human systems in semiarid regions, and have significant implications for on-going revegetation programs implemented in these regions of China.

The Tibetan Plateau snow cover is characterized by rapid changes on a weekly time-scale, which can cause rapid changes in surface albedo. Using snow and surface albedo data from satellite observations, we find that changes in snow coverage on the Tibetan Plateau dominate the rapid changes in surface albedo. However, snow depth also has a distinct effect on rapid changes in surface albedo in some areas especially with unstable snow cover. We test the snow depth-dependent snow albedo parameterization scheme in the land surface model. The results show that whether or not the variation of snow albedo with snow depth is considered directly affects the rapidly changing characteristics of the simulated snow cover on the Tibetan Plateau, which further affects the simulation of surface albedo. These results highlight the rapid response of surface albedo to both snow coverage and depth over the Tibetan Plateau.

Deforestation can impact surface temperature via biophysical processes. Earth system models (ESMs) are commonly used tools to examine biophysical effects of deforestation, but the model capacity to represent deforestation effects remains unclear. In this study, we comprehensively evaluate the performance of four ESMs of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in representing deforestation effects with a satellite‐based benchmark. The results show that the ESMs can basically capture the sign of the temperature response but over‐ or underestimate the magnitude. Such biases are the consequence of biases in the simulated responses of albedo and sensible and latent heat fluxes. Specifically, the ESMs consistently overestimate the albedo response under snow‐covered conditions, for example, in the northern latitudes and in the cold season. The ESMs fail to fully reproduce the observed responses of sensible and latent heat fluxes, and the model bias depends on the model, region and season. The ESMs and observations even disagree on the sign of responses of sensible and latent heat fluxes in some cases. An attribution analysis further shows that biases in the simulated surface temperature response mainly result from biases related to the response of the surface energy partitioning. Biases related to the albedo response only play an important role under snow‐covered conditions. Given these model biases, we highlight that when the CMIP6 models are used to investigate deforestation effects, the simulated result should be interpreted with caution. Moreover, the identified model deficiency shown here also has implications for model improvement.

Afforestation can play a key role in local climate mitigation by influencing local temperature through changes in land surface properties. Afforestation impacts depend strongly on the background climate, with contrasting effects observed across geographical locations, seasons and levels of greenhouse gas-induced warming. Meanwhile, atmospheric aerosols, which are a critical factor influencing regional climate, have varied substantially in recent decades and will continue to change. However, the impacts of aerosol changes on the local effects of afforestation remain unknown. Here, using multiple emissions scenario-based simulations, we show that lower anthropogenic emissions can modulate the local effects of afforestation through modifications in the surface energy balance. If current anthropogenic emissions are reduced to preindustrial levels, afforestation can produce additional cooling effects of up to 0.4 °C. The cooling effects of afforestation are projected to be most strongly affected in China if strict control measures on air pollution are adopted in the future. Our results demonstrate that the enhanced cooling effects of afforestation could partially counteract the warming effect of air quality control, with implications for countries that face the dual challenges of clean air and climate mitigation.

Abstract The Loess Plateau of China has witnessed a remarkable greening trend due to vegetation restoration in recent decades. However, the precipitation response to greening remains unclear, and the hydrological effect of greening is controversial. Here, we revisited biophysical effects of greening on precipitation over the plateau during 2002–2015 using the state‐of‐the‐art water vapor tracer embedded in a regional coupled model. We find that greening can promote the growing season precipitation (0.45 mm·day−1), with 15% and 85% of the precipitation increment resulting from increases in the local evapotranspiration and the water vapor inflow from outside the plateau, respectively. As a consequence, the enhanced precipitation can compensate for the terrestrial water loss driven by the increased evapotranspiration, leading to a slight increase in the water yield. This study highlights the dominant role of the nonlocal effect in precipitation responses to greening in this region.

Afforestation can impact surface temperature through local and nonlocal biophysical effects. However, the local and nonlocal effects of afforestation in China have rarely been explicitly investigated. In this study, we separate the local and nonlocal effects of idealized afforestation in China based on a checkerboard method and the regional Weather Research and Forecasting (WRF) Model. Two checkerboard pattern–like afforestation simulations (AFF1/4 and AFF3/4) with regularly spaced afforested and unaltered grid cells are performed; afforestation is implemented in one out of every four grid cells in AFF1/4 and in three out of every four grid cells in AFF3/4. The mechanisms for the local and nonlocal effects are examined through the decomposition of the surface energy balance. The results show that the local effects dominate surface temperature responses to afforestation in China, with a cooling effect of approximately −1.00°C for AFF1/4 and AFF3/4. In contrast, the nonlocal effects warm the land surface by 0.14°C for AFF1/4 and 0.41°C for AFF3/4. The local cooling effects mainly result from 1) enhanced sensible and latent heat fluxes and 2) decreases in downward shortwave radiation due to increased low cloud cover fractions. The nonlocal warming effects mainly result from atmospheric feedbacks, including 1) increases in downward shortwave radiation due to decreased low cloud cover fractions and 2) increases in downward longwave radiation due to increased middle and high cloud cover fractions. This study highlights that, despite the unexpected nonlocal warming effect, afforestation in China still has great potential in mitigating climate warming through biophysical processes.

Abstract Using Weather Research and Forecasting (WRF) model, we perform the first dynamic downscaling of Community Earth System Model (CESM) Low‐Warming simulations to examine hot extreme changes over China in response to stabilized 1.5°C and 2°C global warming. WRF projects more intense and frequent hot extremes due to global warming, which are qualitatively consistent with CESM. However, WRF and CESM significantly differ in magnitudes of hot extreme changes. Compared to CESM, WRF indicates larger increases in hot extremes over Tibetan Plateau but smaller increases over other regions. Such differences between WRF and CESM are mainly caused by divergent projections of shortwave radiation changes. WRF also suggests fewer benefits achieved from the additional 0.5°C warming constraint than CESM. This study demonstrates the climate risks might be inadequately informed by global climate models and it is necessary to reevaluate impacts of 1.5°C and 2°C global warming on regional climate based on regional climate models.

Using Weather Research and Forecasting (WRF) model, we perform the first dynamic downscaling of Community Earth System Model (CESM) Low-Warming simulations to examine hot extreme changes over China in response to stabilized 1.5 degrees C and 2 degrees C global warming. WRF projects more intense and frequent hot extremes due to global warming, which are qualitatively consistent with CESM. However, WRF and CESM significantly differ in magnitudes of hot extreme changes. Compared to CESM, WRF indicates larger increases in hot extremes over Tibetan Plateau but smaller increases over other regions. Such differences between WRF and CESM are mainly caused by divergent projections of shortwave radiation changes. WRF also suggests fewer benefits achieved from the additional 0.5 degrees C warming constraint than CESM. This study demonstrates the climate risks might be inadequately informed by global climate models and it is necessary to reevaluate impacts of 1.5 degrees C and 2 degrees C global warming on regional climate based on regional climate models. Plain Language Summary Regional climate models (RCMs) are generally more superior than global climate models (GCMs) in climate modeling and projection at regional scales. Using a RCM, we examine the future hot extreme changes over China in response to the global warming being stabilized at 1.5 degrees C and 2 degrees C level by the end of the 21st century. We also compare the result from the RCM with that from a GCM. The RCM and GCM both indicate China will suffer from more intense and frequent hot extreme events due to the 1.5 degrees C and 2 degrees C warming. However, the magnitudes of hot extreme changes are significantly different between the two models. Specifically, the RCM indicates higher risks of hot extremes over Tibetan Plateau and lower risks over other regions than the GCM. Compared to the GCM, the RCM also indicates that fewer benefits can be achieved from the additional 0.5 degrees C warming.