The joint effects of aerosol, thermodynamic, and cloud-related factors on cloud-to-ground lightning in Sichuan were investigated by a comprehensive analysis of ground-based measurements made from 2005 to 2017 in combination with reanalysis data. Data include aerosol optical depth, cloud-to-ground (CG) lightning density, convective available potential energy (CAPE), mid-level relative humidity, lower- to mid-tropospheric vertical wind shear, cloud-base height, total column liquid water (TCLW), and total column ice water (TCIW). Results show that CG lightning density and aerosols are positively correlated in the plateau region and negatively correlated in the basin region. Sulfate aerosols are found to be more strongly associated with lightning than total aerosols, so this study focuses on the role of sulfate aerosols in lightning activity. In the plateau region, the lower aerosol concentration stimulates lightning activity through microphysical effects. Increasing the aerosol loading decreases the cloud droplet size, reducing the cloud droplet collision–coalescence efficiency and inhibiting the warm-rain process. More small cloud droplets are transported above the freezing level to participate in the freezing process, forming more ice particles and releasing more latent heat during the freezing process. Thus, an increase in the aerosol loading increases CAPE, TCLW, and TCIW, stimulating CG lightning in the plateau region. In the basin region, by contrast, the higher concentration of aerosols inhibits lightning activity through the radiative effect. An increase in the aerosol loading reduces the amount of solar radiation reaching the ground, thereby lowering the CAPE. The intensity of convection decreases, resulting in less supercooled water being transported to the freezing level and fewer ice particles forming, thereby increasing the total liquid water content. Thus, an increase in the aerosol loading suppresses the intensity of convective activity and CG lightning in the basin region.

The ecosystems in the mountainous region of Southwest China are exceptionally fragile and constitute one of the global hotspots for wildfire occurrences. Understanding the complex interactions between wildfires and their environmental and anthropogenic factors is crucial for effective wildfire modeling and management. Despite significant advancements in wildfire modeling using machine learning (ML) methods, their limited explainability remains a barrier to utilizing them for in-depth wildfire analysis. This paper employs Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models along with the MODIS global fire atlas dataset (2004–2020) to study the influence of meteorological, topographic, vegetation, and human factors on wildfire occurrences in the mountainous region of Southwest China. It also utilizes Shapley Additive exPlanations (SHAP) values, a method within explainable artificial intelligence (XAI), to demonstrate the influence of key controlling factors on the frequency of fire occurrences. The results indicate that wildfires in this region are primarily influenced by meteorological conditions, particularly sunshine duration, relative humidity (seasonal and daily), seasonal precipitation, and daily land surface temperature. Among local variables, altitude, proximity to roads, railways, residential areas, and population density are significant factors. All models demonstrate strong predictive capabilities with AUC values over 0.8 and prediction accuracies ranging from 76.0% to 95.0%. XGBoost outperforms LR and RF in predictive accuracy across all factor groups (climatic, local, and combinations thereof). The inclusion of topographic factors and human activities enhances model optimization to some extent. SHAP results reveal critical features that significantly influence wildfire occurrences, and the thresholds of positive or negative changes, highlighting that relative humidity, rain-free days, and land use land cover changes (LULC) are primary contributors to frequent wildfires in this region. Based on regional differences in wildfire drivers, a wildfire-risk zoning map for the mountainous region of Southwest China is created. Areas identified as high risk are predominantly located in the Northwestern and Southern parts of the study area, particularly in Yanyuan and Miyi, while areas assessed as low risk are mainly distributed in the Northeastern region.

The basic characteristics of cloud water, precipitation, and the dependence of precipitation efficiency (PE) on the influencing factors over the Tibetan Plateau (TP) are investigated. Results found that the liquid water path shows a significant downward trend in winter over the TP, and the ice water path shows a significant upward trend in the pre‐monsoon and winter seasons and a significant downward trend in the monsoon season in the western TP from 1998 to 2015. In the eastern TP, the precipitation in the monsoon season also shows a significant downward trend, which may be related to the weakening of the South Asian monsoon. Results have determined that precipitation depends more on the ice water cloud than on the liquid water cloud over the TP. Moreover, the convective available potential energy (CAPE) and the low‐tropospheric relative humidity (RH) are two environmental factors that have a prominent influence on the PE. During the monsoon season, higher CAPE and RH were conducive to a larger PE over the TP. The results suggest that the CAPE has a positive effect on the PE, which means that the PE is directly dependent on the convective precipitation, mainly due to the frequent convective activity and dominant convective precipitation over the TP.

Lightning matters to human life and natural fires so much that monitoring and predicting lightning are highly important. A simple proxy for climatic cloud‐to‐ground (CG) density is evaluated defined as the product of convective available potential energy (CAPE) and precipitation rate using data from 2005 to 2017 in Sichuan of Southwest China. CAPE times precipitation rate (CP) relates the monthly distribution and magnitude of negative lightning in the basin region more closely, while CP describes the monthly distribution and magnitude of positive lightning in the plateau region more appropriately. Except for individual years, the annual variation trend and magnitude of CP and positive and negative lightning are relatively consistent in the basin region, while the description of the annual variation trend and magnitude of lightning by CP in the plateau region is insufficient, especially for negative lightning. CP represents the high‐value areas of the positive and negative lightning in the basin and southern Sichuan reasonably, but misses the lightning belt in the plateau region. In general, the performance of CP for lightning in the basin is superior to that in the plateau, this may be caused by two factors, one is the particularity and complexity of lightning in the plateau resulted from special topographic and meteorological conditions, the other one is the limitation of CP caused by the fact that CP does not take into account other environmental factors contributing to lightning. The ability of this simple proxy to describe CG lightning in Sichuan is limited by vertical wind shear in the middle and lower troposphere, zero degree height, and liquid water path, mainly because the proxy does not have the ability to generalize these factors that play a key role in lightning activity.