Incorporating seasonality into livestock spatial distribution is of great significance for studying the complex system interaction between climate, vegetation, water, and herder activities, associated with livestock. The Qinghai-Tibet Plateau (QTP) has the world’s most elevated pastoral area and is a hot spot for global environmental change. This study provides the spatial distribution of cattle, sheep, and livestock grazing on the warm-season and cold-season pastures at a 15 arc-second spatial resolution on the QTP. Warm/cold-season pastures were delineated by identifying the key elements that affect the seasonal distribution of grazing and combining the random forest classification model, and the average area under the receiver operating characteristic curve of the model is 0.98. Spatial disaggregation weights were derived using the prediction from a random forest model that linked county-level census livestock numbers to topography, climate, vegetation, and socioeconomic predictors. The coefficients of determination of external cross-scale validations between dasymetric mapping results and township census data range from 0.52 to 0.70. The data could provide important information for further modeling of human-environment interaction under climate change for this region.

Spatially explicit crop yield datasets with continuous long-term series are essential for understanding the spatiotemporal variation of crop yield and the impact of climate change on it. There are several spatial disaggregation methods to generate gridded yield maps, but these either use an oversimplified approach with only a couple of ancillary data or an overly complex approach with limited flexibility and scalability. This study developed a spatial disaggregation method using improved spatial weights generated from machine learning. When applied to Chinese maize yield, extreme gradient boosting (XGB) derived the best prediction results, with a cross-validation coefficient of determination (R²) of 0.81 at the municipal level. The disaggregated yield at 1 km grids could explain 54% of the variance of the county-level statistical yield, which is superior to the existing gridded maize yield dataset in China. At the site level, the disaggregated yields also showed much better agreement with observations than the existing gridded maize yield dataset. This lightweight method is promising for generating spatially explicit crop yield datasets with finer resolution and higher accuracy, and for providing necessary information for maize production risk assessment in China under climate change.

Evapotranspiration (ET), a crucial component of water consumption in the hydrological process, is directly controlled by soil moisture (SM) and vapor pressure deficit (VPD) from the perspectives of water supply and demand. However, SM and VPD are strongly coupled through multiple physical processes, confounding their effects on ET. Here, we decouple the interaction between SM and VPD and then analyze the spatiotemporal pattern of their individual effects on ET based on multiple observation‐based data sets. The results show that ET is limited by SM rather than VPD in approximately 63% of global land areas (60°S–60°N), defined as water supply‐limited regions. From 1982 to 2014, the effect of SM on ET enhances significantly in 43% of the water supply‐limited regions. The trends can be attributed to changes in SM and VPD themselves as well as to changes in vegetation conditions. Using the findings from the observation‐based data sets as the benchmark, we show that Earth System Models (ESMs) can overall reproduce the spatial pattern of SM and VPD effects on ET but fail to capture their temporal trends. Our results highlight that the water supply and demand control on ET varies with changing environments, which should be explicitly considered when analyzing the terrestrial water cycle and land‐atmosphere interaction.

As a crucial component of the climate system, snow cover plays an important role on surface energy budgets, hydrological cycles and socioeconomic development. This paper investigated the spatiotemporal patterns of snow depth across the Qinghai Plateau (QP) during 1980–2018 based on passive microwave (PMW) satellite observation and reanalysis products. The study identified the relationships between the changes in PMW snow depth and topographic features and revealed the impact of climatic variables (air temperature, precipitation and wind speed) and large‐scale atmospheric circulations on observed snow depth variability. The results show that the spatial pattern of snow depth climatology is similar in all datasets, except in terms of magnitude, with high values in the southern and southeastern parts of the QP and low values in the eastern and northwestern parts. Average snow depth correlates positively with precipitation, elevation and slope, and negatively with air temperature and wind speed. The long‐term trends in snow depth vary with the season and the datasets. The PMW snow depth across the QP shows a significant annual (−0.125 cm·decades⁻¹, p < .10) and spring (−0.184 cm·decades⁻¹, p < .05) negative trend, while snow depth for ERA5 and MERRA2 does not present significant trends. Air temperature dominates total snow depth variation over the QP, explaining 43.85, 24.88 and 47.28% of annual, winter and spring PMW snow depth variations, which significantly affects snow depth variations in most parts of the QP, yet the effects of precipitation and wind speed on snow depth variation exhibit significant regional differences, and atmospheric circulations (e.g., AMO) also have a remarkable controlling effect on some localized areas.

Climate warming affects global livestock productivity. The meat yield from cattle farming (cattle meat per animal) represents livestock productivity at the individual level. However, the impact of warming on cattle meat yield at a global scale is not well understood. In this study, we combine country-level data on the annual meat yield from cattle farming and socioeconomic data from 1961 to 2020 with climate projections from General Circulation Models. The findings show that cattle meat yield increases as temperatures rise from low to medium and then decreases when annual average temperatures exceed 7 °C; this repose is pronounced in the grassland-based livestock system. Further, we show that warming creates unequal impacts between high- and low-income countries due to the divergent baseline temperature conditions. Future warming aggravates these unequal burdens between countries, with the most pronounced effects observed under the upper-middle emissions scenario.

Understanding the changes in crop yield stability (μ/σ) under climate change is critical for food security and farmer livelihoods. Unstable crop yields have been identified as detrimental to international food trade and markets. However, the association between climate extremes and crop yield stability has not been well documented. Here, we present the sensitivity of corn and soybean yield stability to heat, drought and excessive wetness by using statistical modeling for rainfed corn and soybean based on survey yield records in the US Midwest. The results using survey data indicate that increased heat, drought and excessive wetness are collectively associated with reduced yield stability for corn and soybean. We find that the changes in corn and soybean yield stability in the US Midwest are predominantly related to heat stress. Additionally, irrigation can mitigate the yield stability reduction associated with heat and drought. In contrast, well‐irrigated yield is more sensitive to excess wet. Our results highlight the importance of examining the correlation between climate extremes and crop yield stability.