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Evapotranspiration (ET), which relates to hydrology and the energy cycle process of land surface, is a key link of the water cycle and an important item of expenditure in energy balance. There have been multiple state-of-the-art climate models that were included in Phase 6 of the Coupled Model Intercomparison Project (CMIP6), but the performance in estimating the ET of the models is still unclear. Thus, this study evaluated the global terrestrial ET of CMIP6 models. The performance of the models and CMIP6 ensemble mean was compared with the GLEAM v3.3a dataset, and the uncertainties of the ensemble were evaluated using the signal-to-noise ratio (SNR). The results show that there was no perfect model that could perform optimally in all aspects of the comparison, and the performance of the CMIP6 ensemble was better than that of a single model. MIROC6, CESM2, and EC-Earth3 performed satisfactorily in some aspects, whereas the performance was poor in other aspects. GFDL-ESM4 exhibited a relatively poor performance. Most models and the CMIP6 ensemble overestimated ET, and the estimations of different models varied greatly, but the results of most models showed an increasing trend. The CMIP6 ensemble overestimated ET in most regions of the world and may have smoothened the variation in the model estimations. In high latitudes such as northern parts of North America and Eurasia, results of the CMIP6 ensemble and GLEAM were approximately the same. The uncertainty of the CMIP6 ensemble was generally low and the estimation reliability varied according to the geographical region.

As a representative index for monitoring and assessing drought, the Standardized Precipitation Index (SPI) relies on a suitable probability distribution function (PDF) to describe a precipitation series, which allows interregional comparisons following normalization. In this study, we considered nine PDFs (exponential (EXP), extreme value (EV), gamma (GAM), generalized Pareto (GP), logistic (LO), log-logistic (LOL), log-normal (LON), normal (NOR), and Weibull (WEI)) as candidates for use in SPI calculations. Based on monthly precipitation time series data (1961–2015) from 582 stations across China, together with the Kolmogorov–Smirnov (K–S) and Akaike Information Criterion (AIC) methods, differences in optimal PDFs for SPI calculations were compared comprehensively from the perspectives of timescale, record length, and index value. Based on the SPI calculated using the optimal PDF at the 6-month timescale (SPI6-opt), we analyzed the spatiotemporal characteristics of drought trends in China using the Mann–Kendall method. Results indicated both the timescale and the record length would affect the selection of the optimal PDF. The performance of the WEI and GAM distributions was superior to other distributions in describing monthly precipitation (especially for long precipitation records) at short and multiple timescales, respectively. During the entire study period, areas of China with high frequency of drought have transferred from the northwest (1960s), to the northeast (2000s), and to the southwest (most recent 5 years). Trend analysis revealed a noticeable wetting tendency confined mainly to Northwest, Northeast, and Southeast China and a significant trend toward drought in Southwest China and on the North China Plain.

As a core essential climate variable (ECV) in the Global Climate Observing System, land surface temperature (LST) plays a pivotal role in climate change monitoring, urban thermal environment assessment, agricultural management, and ecosystem surveillance. To obtain high-precision LST data, a novel machine learning-based LST downscaling framework integrating feature interaction optimization and Shapley additive explanations (SHAP) scoring was proposed. SHAP scoring was employed for feature importance analysis to identify optimal predictors, while 10 distinct models were comparatively evaluated to establish a high-resolution downscaling framework adaptable to homogeneous surface characteristics. The results show that the SHAP-based feature selection significantly enhanced prediction accuracy by prioritizing nonlinear determinants. The red-blue band interaction feature demonstrated consistent dominance across all algorithms (XGBoost, LightGBM, GradientBoost), exhibiting both the broadest SHAP value range (−2.0 to 2.0) and the highest relative contribution weight. By explicitly addressing spatial heterogeneity, the spatial random forest (SRF) model achieved superior downscaling performance, particularly in vegetated regions. It generated reliable 10 m-resolution LST estimates (R2 = 0.74, RMSE = 6.28°C), demonstrating robust generalization capabilities in complex terrain conditions. The SHAP-based land surface temperature downscaling method can effectively capture the nonlinear interactions among spectral, topographic, and other features, demonstrating high accuracy and strong physical interpretability in high-resolution temperature retrieval over areas dominated by homogeneous vegetation.

In this study, a groundwater exploitation scheme is incorporated into the regional climate model, RegCM4, and the climatic responses to anthropogenic alteration of groundwater are then investigated over the Haihe River Basin in Northern China where groundwater resources are overexploited. The scheme models anthropogenic groundwater exploitation and water consumption, which are further divided into agricultural irrigation, industrial use and domestic use. Four 30-year on-line exploitation simulations and one control test without exploitation are conducted using the developed model with different water demands estimated from relevant socioeconomic data. The results reveal that the groundwater exploitation and water consumption cause increasing wetting and cooling effects on the local land surface and in the lower troposphere, along with a rapidly declining groundwater table in the basin. The cooling and wetting effects also extended outside the basin, especially in the regions downwind of the prevailing westerly wind, where increased precipitation occurs. The changes in the four exploitation simulations positively relate to their different water demands and are highly non-linear. The largest changes in climatic variables usually appear in spring and summer, the time of crop growth. To gain further insights into the direct changes in land-surface variables due to groundwater exploitation regardless of the atmospheric feedbacks, three off-line simulations using the land surface model Community Land Model version 3.5 are also conducted to distinguish these direct changes on the land surface of the basin. The results indicate that the direct changes of land-surface variables respond linearly to water demand if the climatic feedbacks are not considered, while non-linear climatic feedbacks enhance the differences in the on-line exploitation simulations.

The terrestrial hydrological process is an essential but weak link in global/regional climate models. In this paper, the development status, research hotspots and trends in coupled atmosphere-hydrology simulations are identified through a bibliometric analysis, and the challenges and opportunities in this field are reviewed and summarized. Most climate models adopt the one-dimensional (vertical) land surface parameterization, which does not include a detailed description of basin-scale hydrological processes, particularly the effects of human activities on the underlying surfaces. To understand the interaction mechanism between hydrological processes and climate change, a large number of studies focused on the climate feedback effects of hydrological processes at different spatio-temporal scales, mainly through the coupling of hydrological and climate models. The improvement of the parameterization of hydrological process and the development of large-scale hydrological model in land surface process model lay a foundation for terrestrial hydrological-climate coupling simulation, based on which, the study of terrestrial hydrological-climate coupling is evolving from the traditional unidirectional coupling research to the two-way coupling study of “climate-hydrology” feedback. However, studies of fully coupled atmosphere-hydrology simulations (also called atmosphere- hydrology two-way coupling) are far from mature. The main challenges associated with these studies are: improving the potential mismatch in hydrological models and climate models; improving the stability of coupled systems; developing an effective scale conversion scheme; perfecting the parameterization scheme; evaluating parameter uncertainties; developing effective methodology for model parameter transplanting; and improving the applicability of models and high/super-resolution simulation. Solving these problems and improving simulation accuracy are directions for future hydro-climate coupling simulation research.

Warmer temperatures significantly influence crop yields, which are a critical determinant of food supply and human well-being. In this study, a probabilistic approach based on bivariate copula models was used to investigate the dependence (described by joint distribution) between crop yield and growing season temperature (TGS) in the major producing provinces of China for three staple crops (i.e., rice, wheat, and maize). Based on the outputs of 12 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathway 5–8.5, the probability of yield reduction under 1.5 °C and 2 °C global warming was estimated, which has great implications for agricultural risk management. Results showed that yield response to TGS varied with crop and region, with the most vulnerable being rice in Sichuan, wheat in Sichuan and Gansu, and maize in Shandong, Liaoning, Jilin, Nei Mongol, Shanxi, and Hebei. Among the selected five copulas, Archimedean/elliptical copulas were more suitable to describe the joint distribution between TGS and yield in most rice-/maize-producing provinces. The probability of yield reduction was greater in vulnerable provinces than in non-vulnerable provinces, with maize facing a higher risk of warming-driven yield loss than rice and wheat. Compared to the 1.5 °C global warming, an additional 0.5 °C warming would increase the yield loss risk in vulnerable provinces by 2–17%, 1–16%, and 3–17% for rice, wheat, and maize, respectively. The copula-based model proved to be an effective tool to provide probabilistic estimates of yield reduction due to warming and can be applied to other crops and regions. The results of this study demonstrated the importance of keeping global warming within 1.5 °C to mitigate the yield loss risk and optimize agricultural decision-making in vulnerable regions.

Understanding hydrological responses to rising levels of greenhouse gases are essential for climate and impact research. It is, however, often limited by a lack of long record of observational data to provide a basis for understanding the long-term behavior of the climate system. Integrating reconstructed data and (global climate and hydrological) model simulations will help us to better understand the variability of climate and hydrology over timescales ranging from decades to centuries. In this study, we proposed an integrated approach to study flood variability in the upper reach of the Yangtze River over the last millennium to the end of the 21st century. To accomplish this, we first drove hydrological models using the precipitation and temperature from four Global Climate Models (GCM), BCC-CSM1.1, MIROC, MRI-CGCM3, and CCSM4, to simulate daily discharge for the upper reach of the Yangtze River during the period of the last millennium (850–1849), historical period (1850–2005), and a future period (2006–2099). Then, we evaluated whether the modeled precipitation, temperature, and extreme discharge had statistical properties similar to those shown in the documented dry-wet periods, temperature anomalies, and paleoflood records. Finally, we explored the extreme discharge variability using model simulations. The results indicate that: (1) The MIROC-ESM model, differing from the other three GCM models, revealed positive temperature changes from the warm period (Medieval Climate Anomaly; MCA) to the cold period (Little Ice Age; LIA), while the temperature variability of the other models was similar to the records. (2) The BCC-CSM1.1 model performed better than the others regarding correlations between modeled precipitation and documented dry-wet periods. (3) Over most of the subbasins in the upper Yangtze River, the magnitude of extreme discharge in the BCC-CSM1.1 model results showed that there was a decrease from the MCA to the LIA period and an increase in the historical period relative to the cold period, while a future increase was projected by the four GCMs under the influence of climate change.

As a fundamental component in material and energy circulation, precipitation with high resolution and accuracy is of great significance for hydrological, meteorological, and ecological studies. Since satellite measured precipitation is often too coarse for practical applications, it is essential to develop spatial downscaling algorithms. In this study, we investigated two downscaling algorithms based on the Multiple Linear Regression (MLR) and the Geographically Weighted Regression (GWR), respectively. They were employed to downscale annual and monthly precipitation obtained from the Global Precipitation Measurement (GPM) Mission in Hengduan Mountains, Southwestern China, from 10 km × 10 km to 1 km × 1 km. Ground observations were then used to validate the accuracy of downscaled precipitation. The results showed that (1) GWR performed much better than MLR to regress precipitation on Normalized Difference Vegetation Index (NDVI) and Digital Elevation Model (DEM); (2) coefficients of GWR models showed strong spatial nonstationarity, but the spatial mean standardized coefficients were very similar to standardized coefficients of MLR in terms of intra-annual patterns: generally NDVI was positively related to precipitation when monthly precipitation was under 166 mm; DEM was negatively related to precipitation, especially in wet months like July and August; contribution of DEM to precipitation was greater than that of NDVI; (3) residuals’ correction was indispensable for the MLR-based algorithm but should be removed from the GWR-based algorithm; (4) the GWR-based algorithm rather than the MLR-based algorithm produced more accurate precipitation than original GPM precipitation. These results indicated that GWR is a promising method in satellite precipitation downscaling researches and needed to be further studied.

Multimodel ensembles are powerful tools for evaluating agricultural production. Multimodel simulation results provided by the Global Gridded Crop Model Intercomparison (GGCMI) facilitate the evaluation of the grain production situation in China. With census crop yield data, the performance of nine global gridded crop models (GGCMs) in China was evaluated, and the yield gaps of four crops (maize, rice, soybean, and wheat) were estimated. The results showed that GGCMs better simulated maize yields than those of other crops in the northeast, north, northwest, east, and center. GEPIC (CLM-CROP) performed best in simulating maize (wheat) yield in the north, east, and northwest (southwest and south), due to reasonable parameter (cultivar and phenology parameters) settings. Because the rice phenology parameters were calibrated against phenological observation networks and a simple nitrogen limitation index was introduced, ORCHIDEE-CROP performed well in rice yield simulation and soybean yield simulation (center and southwest). Among four crops, wheat has the largest yield gap (7.3–14.1%), in which the poor soil of northwest (14.1%) exposes wheat to relatively high nutritional stress. Thus, in northwest China, optimizing nitrogen management in wheat production can effectively mitigate the negative impact of climate change on crop production.

Quantitative assessment of water quality and its spatial variation identification, as well as the discernment of primary factors affecting water quality are in its urgent in water environment management. In this study, four key water quality indicators,namely, ammonia nitrogen（NH_4~＋-N）, permanganate index（COD_（Mn））, total phosphorus（TP） and total nitrogen（TN） at 71 sampling sites were selected to evaluate water quality and its spatial variation identification. More concerns were emphasized on the anthropogenic factors（land use pattern） and natural factors（river density, elevation and precipitation） to quantify the overall water quality variations at different spatial scales. Results showed that the Yi-Shu-Si River sub-basin had a better water quality status than the Huai River sub-basin. The moderate polluted area nearly distributed in the upper and middle reaches of the Shaying River and Guo River. The high cluster centers which were surrounded with COD_（Mn）, NH_4~＋-N, TN and TP mainly also distributed in the upper and middle reaches of the Shaying River and Guo River. Redundancy analysis showed that the 200 m buffer area acted as the most sensitive area, which was easily subjected to pollution. The precipitation was identified as the most important variables among all the studied hydrological units, followed by farmland, urban land or elevation. The point source pollution was still existed although the non-point source pollution was also identified. The urban surface runoff pollution was severer than farmland fertilizer loss at the sub-basin scale in flood season, while the farmland showed ＂small-scale＂ effects for explaining overall water quality variations. This research is helpful for identifying the overall water quality variations from the scale-process interactions and providing a scientific basis for pollution control and decision making for the Huai River Basin.