Abstract: The choice of rainfall variables is critical for the accuracy of the rainfall threshold model. At the base of 2760 rainfall-induced landslides and daily rainfall data in the study area from 2001 to 2020, a Thiessen polygon was constructed to identify the rainfall that triggered and did not trigger landslides, a total of 1, 053 and 283, 446 respectively. Based on this data, a rainfall threshold model is planned to be constructed by the daily rainfall(R) and the previous effective rainfall(EAR). Second, to select the appropriate variable EAR, five effective antecedent rainfall(EAR)models are used to generate 201 candidate rainfall variables under different parameters k. Probability distribution of rainfall variables in triggered and did not trigger landslide rainfall:P(EAR|L), P(EAR|NL), are obtained by histogram. The conditional probability and prior probability of rainfall-induced landslide:P(L|EAR), P(L) are obtained by the Bayesian formula. Then, four rainfall variable selection methods were used to determine the optimal EAR. Among which: DGSA, GGSA performs variable selection by computing the divergence and information gain between P(L|EAR) and P(EAR|NL). Conditional probability-based correlation analysis performs variable selection by computing the difference in the area under the curve of P(L|EAR) and P(L). The Pearson correlation method performs variable selection by computing the correlation between EAR and P(L|EAR). Finally, we use two linear classifiers and two nonlinear classifiers to obtain the R-EAR rainfall threshold and use the average correct rate to verify the variable selection methods. The results show that: 92.15% of landslides in the study area were induced by rainfall, among which, 46.59% were directly induced by heavy rain, 68.95% were directly or indirectly induced by heavy rain, indicating that heavy rain was the main cause of rainfall-type landslides; 2)There is a significant linear increasing relationship between the importance coefficient calculated by DGSA and GGSA and the performance of the rainfall threshold, the linear correlation is above 0.98, indicating that sensitivity analysis can be used to identify rainfall variables associated with the optimal rainfall threshold; 3)Compared with the Pearson correlation method, DGSA and GGSA are still applicable when the rainfall variable is nonlinear with the frequency of landslides.