Lightning is the primary natural cause of wildfires in mid- to high-latitude forests, and it is increasing in frequency under climate change. Traditional fire danger forecasts, reliant on standard meteorological data, often fail to capture extreme events and future risk. To address this issue, we integrate extreme climate indices with meteorological, vegetation, soil, and topographic data, and apply four machine learning methods to build probabilistic models for lightning fire occurrence. The results show that incorporating extreme climate indices significantly improves model performance. Among the models, XGBoost achieved the highest accuracy (87.4%) and AUC (0.903), clearly outperforming traditional fire weather indices (accuracy 60%–71%). Model interpretation with SHapley Additive exPlanations (SHAP) further revealed the driving mechanisms and interaction effects of extreme factors. Extreme temperature and precipitation indices contributed nearly 60% to fire occurrence, with growing season length (GSL), minimum of daily maximum temperature (TXn), diurnal temperature range (DTR), and warm spell duration index (WSDI) identified as key drivers. In contrast, heavy precipitation indices exerted a suppressing effect. Compound hot and dry conditions amplified fuel aridity and markedly increased ignition probability. This interpretable framework improves short-term lightning fire prediction and offers quantitative support for risk warning and resource allocation in a warming climate.

The Mongolian Plateau, a critical area for global climate change response, faces increasing vulnerability from climate change and human activities impacting its arid ecosystems. This study integrates GeoDetector and machine learning to predict vegetation Carbon Use Efficiency (CUE) dynamics. It utilizes multi-source remote sensing data (MODIS, ERA5-Land) from 2000 to 2020 and incorporates four Shared Socioeconomic Pathways (SSPs) from CMIP6. The results indicate the following: (1) significant spatial variation exists, with high-value CUE areas (≥0.7) in the northwest due to favorable climatic conditions, while low-value areas (<0.6) in the east are affected by decreasing precipitation and overgrazing; (2) CUE increased at an annual rate of 1.03%, with a 43% acceleration after the 2005 climate shift, highlighting the synergistic effects of ecological engineering; (3) our findings reveal that the interaction of evapotranspiration and temperature dominates CUE spatial differentiation, with the random forest model accurately predicting CUE dynamics (root mean square error (RMSE) = 0.0819); (4) scenario simulations show the SSP3-7.0 pathway will peak CUE at 0.6103 by 2050, while the SSP5-8.5 scenario will significantly reduce spatial heterogeneity. The study recommends enhancing water–heat regulation in the northwest and implementing vegetation restoration strategies in the east, alongside establishing a CUE warning system. This research offers valuable insights for improving carbon sequestration and climate resilience in arid ecosystems, with significant implications for carbon management under high-emission scenarios.

Dryland phenology is tightly constrained by water availability, yet the temporal depth of drought influence remains poorly resolved at regional scales. We analyzed the start and end of season across the Mongolian Plateau using 500 m MODIS kNDVI for 2001–2020 and a phenology-anchored framework that linked multi-timescale SPEI directly to the month of each phenological event. By varying accumulation windows and testing month-wise lags up to twelve months, we mapped pixel-level optimal timescales and sensitivities. Phenology exhibits a clear north–south gradient with weak long-term shifts relative to large interannual variability. Drought acts through two pathways. Multi-month winter–spring moisture deficits delay spring green-up, with the strongest SOS sensitivity to antecedent drought about six to nine months prior. Summer–autumn dryness advances dormancy, and EOS is governed mainly by near-term moisture over the previous one to two months. Responses differ among ecoregions, with deserts and desert steppes the most sensitive and forests and alpine meadows less responsive. These asymmetric timescales imply that prolonged deficits can postpone spring emergence into the following year, whereas short deficits truncate the current season, offsetting warming-driven extensions of growing-season length. Incorporating phenology-anchored, multi-timescale drought indicators can improve model forecasts of dryland carbon–water dynamics and inform monitoring and adaptation in the most water-limited ecoregions.

Desertification presents a significant ecological challenge in arid and semi-arid regions, posing a severe threat to regional ecological security and sustainable development. This study introduces an integrated framework for desertification vulnerability assessment, combining the MEDALUS model with the XGBoost algorithm, to evaluate desertification dynamics across the Mongolian Plateau from 2000 to 2020 and project future trends under four Shared Socioeconomic Pathways (SSPs) for 2030. The findings are as follows: (1) Between 2000 and 2020, desertification vulnerability was most pronounced in the southern and western regions of the plateau, with lower vulnerability observed in the northern and eastern areas. High-vulnerability zones expanded over time, highlighting the need for targeted and prioritized management efforts. (2) Climate factors—particularly temperature, wind speed, and precipitation—emerged as the dominant drivers of desertification, followed by soil characteristics and vegetation (NDVI). The influence of human activities on desertification became increasingly significant, stressing the need for improved land management and sustainable practices. (3) Future risks show that desertification vulnerability in the Mongolian Plateau will intensify under high-emission scenarios (SSP3-7.0, SSP5-8.5), with significant expansion of high vulnerability areas. Lower-emission scenarios (SSP1-2.6, SSP2-4.5) may reduce some impacts, but high vulnerability will persist, highlighting the need for urgent climate mitigation and adaptation efforts.

Desertification intensified by climate change and human activities poses major challenges to biodiversity conservation in dryland regions. In these areas, habitat fragmentation and weak spatial planning and management result in poor connectivity and low protection effectiveness of protected areas (PAs), hindering long-term biodiversity conservation and ecosystem stability. Therefore, based on the evolution of the PA network in the drylands of Central and East Asia from 2000 to 2030, this study integrates circuit theory and the MaxEnt model to systematically assess the protection capacity of PAs. A multi-dimensional optimization model—spatiotemporal change–connectivity–protection effectiveness (SCPE)—is proposed to optimize the PA network from three perspectives: spatial structure, functional performance, and climate adaptability. The results show that network connectivity improves most significantly under the SSP1-2.6 scenario, while protection effectiveness declines across all scenarios, indicating a growing spatial mismatch between PA distribution and biodiversity hotspots. Optimization based on the SCPE model effectively alleviates this mismatch, with the lowest demand for new corridors and buffer zones under SSP1-2.6 and the highest under SSP5-8.5. After optimization, IIC and PC increase by 49.90% and 49.41%, respectively, under SSP1-2.6, significantly enhancing network resilience. The findings demonstrate that the SCPE model, driven by function-oriented strategies, can effectively improve the connectivity and climate responsiveness of PA networks. This provides scientific support for adaptive conservation planning under climate change and offers practical tools for biodiversity conservation in dryland ecosystems.