Climate-related extreme events are exerting significant pressure on food security in the Asia-Pacific Region, home to over half of the world’s population. China, Thailand, Vietnam, and many Southeast Asian countries are major producers and exporters of food globally. Recent climatic extremes associated with El Niño have caused significant crop losses, further compounded by war and trade embargoes that threaten food security. To address these challenges, four measures are essential: (i) Taking action on climate adaptation; (ii) Coordinated, rules-based regional food-aid mechanisms; (iii) Protecting domestic production and conserving soils, and (iv) Advancing zero-carbon farming practices. Together, these strategies are necessary to sustain future food security in the region.

Drought represents one of the most critical environmental hazards in arid and semi-arid regions, exerting profound impacts on ecological security and sustainable development. Nevertheless, many existing drought indices exhibit delayed responses to precipitation and soil moisture anomalies, thereby constraining their ability to characterize the rapid onset and evolution of drought events. To address this limitation, we propose the Standardized Temperature–Vegetation Drought Index (STVDI), which integrates precipitation, evapotranspiration, temperature, and vegetation dynamics within a Euclidean space framework and explicitly incorporates lag-response analysis. Taking the Mongolian Plateau (MP)—a key transition zone from taiga forest to desert steppe—as the study region, we constructed a 1 km resolution STVDI dataset spanning 2000–2021. Results reveal that over 88% of the MP is highly susceptible to flash droughts, with an average lag time of only 0.52 days, underscoring the index’s capacity for rapid drought detection. Spatial analysis indicates that drought severity peaks during March and April, with moderate drought conditions concentrated in central Mongolia and severe droughts prevailing across southwestern Inner Mongolia. Although trend analysis suggests a slight long-term alleviation of drought intensity, nearly 70% of the MP is projected to experience further intensification in the future. This study delivers the first high-resolution, low-lag drought monitoring dataset for the MP and advances theoretical understanding of drought propagation and lag mechanisms in arid and semi-arid ecosystems.

      Land degradation is an important ecological and environmental problem facing the world. “Land Degradation Neutrality” is one of the core indicators in the United Nations Sustainable Development Goals for 2030. However, achieving this is a serious challenge in Mongolia where land degradation continues. The increasingly serious land degradation in Mongolia has had a direct impact on the ecology of the entire Mongolian plateau and adjacent regions. Land degradation and restoration in this region fluctuate spatially and temporally because of the impacts of global climate change and human activity.      We obtained land cover data for Mongolia for 1990, 2000, 2010, and 2015 with a resolution of 30 m using the object-oriented remote sensing image interpretation method.Land cover types include forest, real steppe, meadow steppe, desert steppe, cropland, built area, water, sand, and barren land. Based on a spatial analysis module in a geographic information system, the multi-period land cover data were superimposed and calculated. We defined the land degradation cover types and restoration cover types in the processing. Thus, a serials of high-resolution distribution maps of land degradation and restoration for fixed monitoring time intervals were obtained for first time.      We analyzed trends in land degradation and restoration and estimated the typical areas of each in Mongolia. We specifically analyzed the process of land cover change in these areas, comprehensively considered natural factors and human activities driving this change. Finally, we proposed targeted strategies to control the land degradation and promote land restoration in different regions in Mongolia.

Vegetation phenology is one of the sensitive indicators reflecting global climate change and vegetation growth. Inner Mongolia is an important part of the ecological security barrier of the Mongolian Plateau, and a key area for resource development, environmental protection and ecological security in China. The study on the vegetation phenological changes of Inner Mongolia is of great significance for understanding the characteristics of climate change and extreme climate events in Inner Mongolia. Based on the normalized differential vegetation index (NDVI) in the high-spatial resolution MOD13Q1 data product, we used Google Earth Engine platform to process MODIS-NDVI raw data for format conversion, projection conversion and clipping, and exported NDVI long time series data from 2000 to 2021. Then, we adopted the dynamic threshold method to obtain a dataset of vegetation phenology of Inner Mongolia from 2001 to 2020. With a resolution of 250 m, this dataset contains remote sensing monitoring data of the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS) in Inner Mongolia from 2001 to 2019. It can provide data support for understanding the temporal and spatial variation of vegetation phenology in Inner Mongolia and its response to climate change.