Central Asia is prone to wildfires, but the relationship between wildfires and climatic factors in this area is still not clear. In this study, the spatiotemporal variation in wildfire activities across Central Asia during 1997–2016 in terms of the burned area (BA) was investigated with Global Fire Emission Database version 4s (GFED4s). The relationship between BA and climatic factors in the region was also analyzed. The results reveal that more than 90% of the BA across Central Asia is located in Kazakhstan. The peak BA occurs from June to September, and remarkable interannual variation in wildfire activities occurs in western central Kazakhstan (WCKZ). At the interannual scale, the BA is negatively correlated with precipitation (correlation coefficient r = −0.66), soil moisture (r = −0.68), and relative humidity (r = −0.65), while it is positively correlated with the frequency of hot days (r = 0.37) during the burning season (from June to September). Composite analysis suggests that the years in which the BA is higher are generally associated with positive geopotential height anomalies at 500 hPa over the WCKZ region, which lead to the strengthening of the downdraft at 500 hPa and the weakening of westerlies at 850 hPa over the region. The weakened westerlies suppress the transport of water vapor from the Atlantic Ocean to the WCKZ region, resulting in decreased precipitation, soil moisture, and relative humidity in the lower atmosphere over the WCKZ region; these conditions promote an increase in BA throughout the region. Moreover, the westerly circulation index is positively correlated (r = 0.53) with precipitation anomalies and negatively correlated (r = −0.37) with BA anomalies in the WCKZ region during the burning season, which further underscores that wildfires associated with atmospheric circulation systems are becoming an increasingly important component of the relationship between climate and wildfire.

Lightning is one of the most severe natural disasters, characterized by its sudden onset, short duration, and significant damage. Existing quality control (QC) schemes for millisecond-level lightning observation data from a single source are primarily limited by the instrument and equipment, leading to inadequate monitoring, forecasting, and early warning accuracy in severe convective weather. This study proposes a comprehensive QC scheme for lightning location data from the China Meteorological Administration ground-based National Lightning Detection Network (CMA-LDN). The scheme integrates radar composite reflectivity (CREF) and FY-4A cloud-top brightness temperature (TBB), exploring the coupled relationship between lightning activity and severe weather processes. Through experimental analysis of convective processes over different time periods, QC thresholds are established based on the CREF, TBB, and area ratio. In this research, CREF ≥ 10 dBZ, TBB ≤ 270 K, and an 80% area ratio are tuned to filter false signals. Based on the regional threshold and area ratio results, gross error elimination and spatiotemporal clustering are combined to achieve an overall QC rate of 28.7%. The most effective quality control (QC) method is spatial-temporal clustering, achieving a QC efficiency of 20.9%. The processed lightning data are further merged with CREF and generated a 1 km and 6 min resolution lightning location dataset, which significantly improves the accuracy of ground-based lightning detection and supports operational forecasting of severe convective weather.