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Drylands cover 41% of Earth’s surface and are the largest source of interannual variability in the global carbon sink. Drylands are projected to experience accelerated expansion over the next century, but the implications of this expansion on variability in gross primary production (GPP) remain elusive. Here we show that by 2100 total dryland GPP will increase by 12 ± 3% relative to the 2000–2014 baseline. Because drylands will largely expand into formerly productive ecosystems, this increase in dryland GPP may not increase global GPP. Further, GPP per unit dryland area will decrease as degradation of historical drylands outpaces the higher GPP of expanded drylands. Dryland expansion and climate-induced conversions among sub-humid, semi-arid, arid, and hyper-arid subtypes will lead to substantial changes in regional and subtype contributions to global dryland GPP variability. Our results highlight the vulnerability of dryland subtypes to more frequent and severe climate extremes and suggest that regional variations will require different mitigation strategies. Earth’s drylands are expected to expand due to climate change, but how this will affect vegetation remains unclear. Here the authors use models to show that despite expansion, primary productivity in drylands is likely to increase through the 21st Century.

Climate change is causing drylands to expand and this work shows that they will cover half of the land surface by 2100 under a moderate emissions scenario. Drylands are home to more than 38% of the total global population and are one of the most sensitive areas to climate change and human activities 1 , 2 . Projecting the areal change in drylands is essential for taking early action to prevent the aggravation of global desertification 3 , 4 . However, dryland expansion has been underestimated in the Fifth Coupled Model Intercomparison Project (CMIP5) simulations 5 considering the past 58 years (1948–2005). Here, using historical data to bias-correct CMIP5 projections, we show an increase in dryland expansion rate resulting in the drylands covering half of the global land surface by the end of this century. Dryland area, projected under representative concentration pathways (RCPs) RCP8.5 and RCP4.5, will increase by 23% and 11%, respectively, relative to 1961–1990 baseline, equalling 56% and 50%, respectively, of total land surface. Such an expansion of drylands would lead to reduced carbon sequestration and enhanced regional warming 6 , 7 , resulting in warming trends over the present drylands that are double those over humid regions. The increasing aridity, enhanced warming and rapidly growing human population will exacerbate the risk of land degradation and desertification in the near future in the drylands of developing countries, where 78% of dryland expansion and 50% of the population growth will occur under RCP8.5.

The diurnal variation in precipitation is an essential aspect of the hydrological cycle. It is also an effective way to assess the model performance and understand the local climate. However, there is still a lack of detailed evaluation and in-depth analyses of precipitation at the sub-daily scale for kilometer-scale dynamical downscaling simulations. In this study, we evaluated the precipitation outputs from quarter-degree dynamical downscaling modeling (28kmDDM) and kilometer-scale dynamical downscaling modeling (4kmDDM) at the sub-daily scale over the Tibetan Plateau (TP) during the summer of 2014. The advantages and disadvantages of kilometer-scale simulation are well-dissected. Our findings indicated that 4kmDDM is clearly advantageous for capturing daily-scale precipitation features, primarily due to its superior ability to simulate convective precipitation during the day. On the contrary, compared to the station observations, 4kmDDM exhibits a nocturnal underestimation, particularly in the southeastern basins of the TP, where steep topographic relief exists. Compared to 28kmDDM, 4kmDDM simulates more realistic surface air temperatures and abundant precipitable water vapor. However, the formation of low-level clouds in 4kmDDM is limited due to insufficient condensation. Under stable nocturnal stratification, 4kmDDM lacks the potential to trigger more condensation in the middle and high layers without low-level condensation latent heating. Fewer clouds and hydrometeors are the primary factors contributing to nocturnal precipitation underestimation. This study highlights the discrepancies of the kilometer-scale simulation in nocturnal precipitation under stable stratification.

Climate change and large‐scale ecological restoration programs have profoundly influenced vegetation greening and gross primary productivity (GPP) in China's drylands. However, the specific pathways through which climatic factors and vegetation greening influence GPP remain poorly understood. This study examines the spatiotemporal changes in GPP across China's drylands from 2001 to 2020 and investigates the direct and indirect effects of climatic factors and leaf area index (LAI) on GPP. The results reveal that the overall improvement in vegetation cover has positively increased GPP in these regions. Although the direct effects of climatic factors on GPP are minimal, they exert a substantial indirect effect by regulating vegetation growth, highlighting that LAI is a key intermediary in mediating the effects of climatic factors on GPP. Furthermore, these complex interactions vary significantly along the aridity gradient. This study emphasizes the necessity of comprehensively considering the intricate interactions among multiple climate and vegetation factors. Plain Language Summary China's drylands have undergone significant vegetation greening and ecological restoration, characterized by transitions toward forests, grasslands, and croplands. These changes have greatly enhanced gross primary productivity (GPP), a key indicator of ecosystem health and functionality. This study reveals that the increase in GPP results from the combined effects of climate change and improved vegetation cover. Although climatic factors like temperature, precipitation, and solar radiation directly affect GPP to a lesser extent, they indirectly boost it by altering vegetation growth conditions. Among the various factors, the increase in vegetation cover has the most direct and substantial positive effect on GPP, especially in semi‐arid and dry sub‐humid regions, where ecological restoration efforts are concentrated. Furthermore, the study indicates that the center of gravity for vegetation productivity in China's drylands is gradually shifting westward, and predicts that most areas will maintain the current trend of increasing vegetation productivity. Overall, under the dual impetus of climate change and greening initiatives, the vegetation in China's drylands has exhibited strong vitality. This not only benefits ecological environment improvement but also supports climate change mitigation and contributes to carbon peaking and carbon neutrality goals. Key Points The trend of gross primary productivity in China's drylands has shown a marked increase, especially after 2011 The leaf area index serves as a crucial intermediary in modulating the indirect effects of climatic factors on gross primary productivity The complex interactions between climatic factors and the leaf area index on gross primary productivity vary along the aridity gradient

Land surface temperature (LST) is a critical thermal variable of the ground surface. However, accurate LST simulation is still challenging over the Tibetan Plateau (TP), having a large cold bias in many global and regional climate models (especially in the winter season). In this study, the LST in winter simulated by WRF was compared to three global land data assimilation datasets (GLDAS), and two reanalysis datasets (ERA-Interim and ERA5). All of these datasets were evaluated against satellite observation. Three GLDAS datasets generally outperform the WRF simulation and reanalysis datasets with smaller cold biases and were taken as the references in the attribution analysis. By decomposing the LST biases using the decomposed temperature metric (DTM), we investigated the contributions of relevant factors to the cold surface temperature biases and the underlying mechanisms. Result shows that the too-less incoming longwave radiation (LW) contributes the most to cold biases, and too-bright surface albedo effect ranks the second. Comparison with MODIS demonstrates an underestimation in the simulated cloud fractions (CF), causing the large contribution of LW simulation to the cold biases. Using a developed neural network-based scale-adaptive CF parameterization, the cold bias over the mainland of TP is greatly reduced. In addition, the improvement of the snow cover fraction (SCF) parameterization leads to the surface albedo decreasing and sensible heat flux increasing, the cold bias can also be reduced by half. Reduction of simulated cold bias possesses great significance and implications in water resources responses over high mountain to global warming.

Deep convective systems (DCSs) are associated with severe weather events and can affect regional and global climate. To study the semi-diurnal variation of DCSs over Eastern China and its surrounding seas in summer, we modified the Tracking of Organized Convection Algorithm through a 3-D segmentatioN (TOOCAN) by employing Himawari-8 operational cloud property (CLP) products instead of original infrared images, and renamed the algorithm as TOOCAN-CLP. The DCSs detected over land and sea are divided into small-, medium-, and large-sized classes based on the convective core equivalent radius. The small and medium-sized DCSs over land exhibit a maximum occurrence in the afternoon, which is associated with local thermal instability and sea breeze circulation. The occurrence of small DCSs over the tropical sea areas varies analogously to that of small continental DCSs but with a smaller amplitude. However, medium-sized DCSs over the sea, which account for the majority of DCSs over the sea, exhibit weak semi-diurnal variability. Large DCSs over inland China and its surrounding seas tend to initiate at night and decay in the daytime. The generation of large DCSs over inland China at night is mainly due to the enhanced transport of warm and moist air by strong large-scale prevailing southerly or southwesterly winds, while the large offshore DCSs accompanied by heavy rainfall is closely associated with the interaction between local offshore breeze and large-scale monsoon flows, as well as gravity waves.

Satellite-based rapid sweeping screening of localized PM2.5 hotspots at fine-scale local neighborhood levels is highly desirable. This motivated us to develop a random forest–convolutional neural network–local contrast normalization (RF–CNN–LCN) pipeline that detects local PM2.5 hotspots at a 300 m resolution using satellite imagery and meteorological information. The RF–CNN joint model in the pipeline uses three meteorological variables and daily 3 m/pixel resolution PlanetScope satellite imagery to generate daily 300 m ground-level PM2.5 estimates. The downstream LCN processes the estimated PM2.5 maps to reveal local PM2.5 hotspots. The RF–CNN joint model achieved a low normalized root mean square error for PM2.5 of within ~31% and normalized mean absolute error of within ~19% on the holdout samples in both Delhi and Beijing. The RF–CNN–LCN pipeline reasonably predicts urban PM2.5 local hotspots and coolspots by capturing both the main intra-urban spatial trends in PM2.5 and the local variations in PM2.5 with urban landscape, with local hotspots relating to compact urban spatial structures and coolspots being open areas and green spaces. Based on 20 sampled representative neighborhoods in Delhi, our pipeline revealed an annual average 9.2 ± 4.0 μg m−3 difference in PM2.5 between the local hotspots and coolspots within the same community. In some cases, the differences were much larger; for example, at the Indian Gandhi International Airport, the increase was 20.3 μg m−3 from the coolest spot (the residential area immediately outside the airport) to the hottest spot (airport runway). This work provides a possible means of automatically identifying local PM2.5 hotspots at 300 m in heavily polluted megacities and highlights the potential existence of substantial health inequalities in long-term outdoor PM2.5 exposures even within the same local neighborhoods between local hotspots and coolspots.

In recent years, electroencephalogram (EEG) signals have been used as a biometric modality, and EEG-based biometric systems have received increasing attention. However, due to the sensitive nature of EEG signals, the extraction of identity information through processing techniques may lead to some loss in the extracted identity information. This may impact the distinctiveness between subjects in the system. In this context, we propose a new self-relative evaluation framework for EEG-based biometric systems. The proposed framework aims at selecting a more accurate identity information when the biometric system is open to the enrollment of novel subjects. The experiments were conducted on publicly available EEG datasets collected from 108 subjects in a resting state with closed eyes. The results show that the openness condition is useful for selecting more accurate identity information.

Current passive sensors fail to accurately identify cloud phase, thus largely limiting the quantification of radiative contributions and precipitation of different cloud phases over the Tibet Plateau (TP), especially for the mixed-phase and supercooled water clouds. By combining the 4 years of (January 2007–December 2010) cloud phase (2B-CLDCLASS-LIDAR), radiative fluxes (2B-FLXHR-LIDAR), and precipitation (2C-PRECIP-COLUMN) products from CloudSat, this study systematically quantifies the radiative contribution of cloud phases and precipitation over the TP. Statistical results indicate that the ice cloud frequently occurs during the cold season, while mixed-phase cloud fraction is more frequent during the warm season. In addition, liquid clouds exhibit a weak seasonal variation, and the relative cloud fraction is very low, but supercooled water cloud has a larger cloud distribution (the value reaches about 0.24) than those of warm water clouds in the eastern part of the TP during the warm season. Within the atmosphere, the ice cloud has the largest radiative contribution during the cold season, the mixed-phase cloud is the second most important cloud phase for the cloud radiative contribution during the warm season, and supercooled water clouds’ contribution is particularly important during the cold season. In particular, the precipitation frequency over the TP is mainly dominated by the ice and mixed-phase clouds and is larger over the southeastern part of the TP during the warm season.

The combined effects of meteorological factors and aerosol chemical compositions on atmospheric visibility in Shanghai were investigated in this study based on the observed hourly dataset during 2022–2024. Correlation analysis and random forest modeling are employed to quantify the relative contributions of these factors. The results reveal significant negative correlations between visibility and both PM2.5 concentration and relative humidity, with partial correlation coefficient of −0.62 and −0.61. Nitrate, ammonium, and other aerosol components substantially modulate these relationships. The random forest model explains 83% of the variance when only meteorological variables are considered, increasing to 93% with the inclusion of aerosol chemical composition. Under 30 km high-visibility conditions, PM2.5 is the dominant predictor (39%) of atmospheric visibility variation, followed by relative humidity (35%). In contrast, during low-visibility conditions (lower than 7.5 km), relative humidity becomes the primary contributor (30%), the influence of PM2.5 weakens (18%), and aerosol chemical components account for a larger share (30%). These findings provide important insights into the mechanisms governing visibility variability under different environmental conditions.