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Abstract Cities are main carbon emissions generators. Land use changes can not only affect terrestrial ecosystems carbon, but also anthropogenic carbon emissions. However, carbon monitoring at a spatial level is still coarse, and low-carbon land use encounters the challenge of being unable to adjust at the patch scale. This study addresses these limitations by using land-use data and various auxiliary data to explore new methods. The approach involves developing a high-resolution carbon monitoring model and investigating a patch-scale low-carbon land use model by integrating high carbon sink/source images with the Future Land Use Simulation model. Between 2000 and 2020, the results reveal an increasing trend in both carbon emissions and carbon sinks in the Shangyu district. Carbon sinks can only offset approximately 3% of the total carbon emissions. Spatially, the north exhibits net carbon emissions, while the southern region functions more as a carbon sink. A total of 14.5% of the total land area witnessed a change in land-use type, with the transfer-out of cropland constituting the largest area at 96.44 km2, accounting for 50% of the total transferred area. Land-use transfer resulted in an annual increase of 77.72 × 104 t in carbon emissions between 2000 and 2020. Through land-use structure optimisation, carbon emissions are projected to increase by only 7154 t C/year from 2000 to 2030, significantly lower than the amount between 2000 and 2020. Further low-carbon land optimisation at the patch scale can enhance the carbon sink by 129.59 t C/year. The conclusion drawn is that there is considerable potential to reduce carbon emissions through land use control. The new methods developed in our study can effectively contribute to high-resolution carbon monitoring in spatial contexts and support low-carbon land use, promoting the application of low-carbon land use from theory to practice. This will provide technological guidance for land use planning, city planning, and so forth.

Abstract Amidst the escalating impacts of global warming, the occurrence and severity of compound extreme weather events have risen significantly, presenting substantial threats to both lives and property. Existing response strategies predominantly focus on individual events, often overlooking the cumulative effects rising from their inherent complexity. To address this critical gap, we conducted a thorough examination of sequential extreme precipitation–heatwave compound events (SEPHCE) in China from 1975 to 2020, utilizing data from 1929 meteorological stations. Our investigation revealed a consistent rise in the frequency and duration of SEPHCE, with a particularly notable surge since 1993. Furthermore, shorter interval events disproportionately affected the regions of southwestern and southeast coastal China. Furthermore, SEPHCE onset times exhibited advancement, and the endings were delayed, thereby intensifying the overall trend. These findings underscore the pressing need to prioritize effective planning and adaptation strategies to mitigate the impact of these compound event, while also addressing the potential exacerbation of inequality resulting from climate change.

Abstract The future state of drought in China under climate change remains uncertain. This study investigates drought events, focusing on the region of China, using simulations from five global climate models (GCMs) under three Shared Socioeconomic Pathways (SSP1-2.6, SSP3-7.0, and SSP5-8.5) participating in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b). The daily Standardized Precipitation Evapotranspiration Index (SPEI) is employed to analyze drought severity, duration, and frequency over three future periods. Evaluation of the GCMs’ simulations against observational data indicates their effectiveness in capturing historical climatic change across China. The rapid increase in CO2 concentration under high-emission scenarios in the mid- and late-future century (2040–2070 and 2071–2100) substantially influences vegetation behavior via regulation on leaf stomata and canopy structure. This regulation decelerates the increase in potential evapotranspiration, thereby mitigating the sharp rise in future drought occurrences in China. These findings offer valuable insights for policymakers and stakeholders to develop strategies and measures for mitigating and adapting to future drought conditions in China.

A timely and accurately predicted grain yield can ensure regional and global food security. The scientific community is gradually advancing the prediction of regional-scale maize yield. However, the combination of various datasets while predicting the regional-scale maize yield using simple and accurate methods is still relatively rare. Here, we have used multi-source datasets (climate dataset, satellite dataset, and soil dataset), lasso algorithm, and machine learning methods (random forest, support vector, extreme gradient boosting, BP neural network, long short-term memory network, and K-nearest neighbor regression) to predict China’s county-level maize yield. The use of multi-sourced datasets advanced the predicting accuracy of maize yield significantly compared to the single-sourced dataset. We found that the machine learning methods were superior to the lasso algorithm, while random forest, extreme gradient boosting, and support vector machine represented the most preferable methods for maize yield prediction in China (R2 ≥ 0.75, RMSE = 824–875 kg/ha, MAE = 626–651 kg/ha). The climate dataset contributed more to the prediction of maize yield, while the satellite dataset contributed to tracking the maize growth process. However, the methods’ accuracies and the dominant variables affecting maize growth varied with agricultural regions across different geographic locations. Our research serves as an important effort to examine the feasibility of multi-source datasets and machine learning techniques for regional-scale maize yield prediction. In addition, the methodology we have proposed here provides guidance for reliable yield prediction of different crops.

Climate warming hampers grassland growth, particularly in dryland regions. To preserve robust grassland growth and ensure the resilience of grassland in these arid areas, a comprehensive understanding of the interactions between vegetation and climate is imperative. However, existing studies often analyze climate–vegetation interactions using concurrent vegetation indices and meteorological data, neglecting time-lagged influences from various determinants. To address this void, we employed the random forest machine learning method to predict the grassland NDVI (Normalized Difference Vegetation Index) in Asian drylands (including five central Asia countries, the Republic of Mongolia, and Parts of China) from 2001 to 2020, incorporating time-lag influences. We evaluated the prediction model’s performance using three indexes, namely the coefficient of determination (R2), root-mean-square error (RMSE), and Mean Absolute Error (MAE). The results underscore the superiority of the model incorporating time-lag influences, demonstrating its enhanced capability to capture the grassland NDVI in Asian drylands (R2 ≥ 0.915, RMSE ≤ 0.033, MAE ≤ 0.019). Conversely, the model without time-lag influences exhibited relatively poor performance, notably inferior to the time-lag-inclusive model. The latter result aligns closely with remote sensing observations and more accurately reproduces the spatial distributions of the grassland NDVI in Asian drylands. Over the study period, the grassland NDVI in Asian drylands exhibited a weak decreasing trend, primarily concentrated in the western region. Notably, key factors influencing the grassland NDVI included the average grassland NDVI in the previous month, total precipitation in the current month, and average soil moisture in the previous month. This study not only pioneers a novel approach to predicting grassland growth but also contributes valuable insights for formulating sustainable strategies to preserve the integrity of grassland ecosystems.

Improved agricultural production systems, together with increased grain yield, are essential to feed the growing global population in the 21st century. Global gridded crop models (GGCMs) have been extensively used to assess crop production and yield simulation on a large geographical scale. However, GGCMs are less effective when they are used on a finer scale, significantly limiting the precision in capturing the yearly maize yield. To address this issue, we propose a relatively more advanced approach that downsizes GGCMs by combining machine learning and crop modeling to enhance the accuracy of maize yield simulations on a regional scale. In this study, we combined the random forest algorithm with multiple data sources, trained the algorithm on low-resolution maize yield simulations from GGCMs, and applied it to a finer spatial resolution on a regional scale in China. We evaluated the performance of the eight GGCMs by utilizing a total of 1046 county-level maize yield data available over a 30-year period (1980–2010). Our findings reveal that the downscaled models created for maize yield simulations exhibited a remarkable level of accuracy (R2 ≥ 0.9, MAE < 0.5 t/ha, RMSE < 0.75 t/ha). The original GGCMs performed poorly in simulating county-level maize yields in China, and the improved GGCMs in our study captured an additional 17% variability in the county-level maize yields in China. Additionally, by optimizing nitrogen management strategies, we identified an average maize yield gap at the county level in China ranging from 0.47 to 1.82 t/ha, with the south maize region exhibiting the highest yield gap. Our study demonstrates the high effectiveness of machine learning methods for the spatial downscaling of crop models, significantly improving GGCMs’ performance in county-level maize yield simulations.