In order to fully explore the correlation between microclimate and transmission line tripping, this paper proposed a method of early warning for microclimate-based tripping and cause quantitative analysis for transmission line. On the basis of meteorological history data, the probability prediction model of microclimate and transmission line tripping was constructed before the tripping based on Light Gradient Boosting Machine (LightGBM). From the circuit level, higher weights were given to the instable samples in LightGBM loss function to cope with the unba-lanced data sets, and the line resistance difference punishment item was added to cut thin lines for learning. After the tripping, the confidence degree of the micrometeorology to the tripping event was quantified based on the Sigmoid cloud model, and the micrometeorology coupling coefficient was constructed at the sectional level to achieve the quantitative analysis of the cause of the tripping. The proposed method can provide real-time early warning for tripping events under given micrometeorology conditions and further quantify the contribution of each micrometeoro-logy to trigger tripping events, thus providing decision support for maintenance work.