Climate change may alter the frequency and intensity of rainfall, and thereby significantly affect the frequency and magnitude of shallow landslides. To accurately evaluate the temporal probability of landslide initiation, it is therefore important to consider the effect of climate variation. Although various approaches have been proposed to estimate the temporal probability of landslides to date, most of them were based on the stationary assumption, i.e., that the statistical properties of the historical rainfall data are time-invariant. However, if historical rainfall data show nonstationary characteristics such as a trend or an abrupt change, the stationary assumption is no longer valid and induces a miscalculation. In this study, we propose a new approach that can estimate the temporal probability of future rainfall-induced landslide occurrence while incorporating the nonstationary characteristics of the rainfall data. In assessing such data, a nonstationary generalized extreme value distribution was used to evaluate the temporal probability. Then, by combining the derived nonstationary temporal probability with landslide susceptibility results obtained from the random forest model, probabilities of landslide occurrence were calculated for future periods, from 1 to 50 years, and compared with the results based on a stationary model. The results showed that the stationary model underestimated the landslide probability compared with the nonstationary approach. This is because an increasing trend in local rainfall, taken from a gauge in the study area, was not considered in the stationary analysis. Thus, climate change has ongoing consequences for landslide occurrence. To reflect the impacts of climate change, a nonstationary approach capable of coping with climate variation should therefore be considered in any landslide hazard analysis.

气候变化可能会改变降雨的频率和强度，从而显着影响浅层滑坡的频率和强度。为了准确评估滑坡发生的时间概率，考虑气候变化的影响很重要。尽管迄今为止已经提出了各种方法来估计滑坡的时间概率，但大多数都是基于平稳假设，即历史降雨数据的统计特性是时间不变的。然而，如果历史降雨数据显示非平稳特征，例如趋势或突变，平稳假设不再有效并导致计算错误。在这项研究中，我们提出了一种新方法，可以估计未来降雨引发的滑坡发生的时间概率，同时结合降雨数据的非平稳特征。在评估此类数据时，使用非平稳广义极值分布来评估时间概率。然后，通过将导出的非平稳时间概率与从随机森林模型获得的滑坡敏感性结果相结合，计算未来 1 到 50 年的滑坡发生概率，并与基于平稳模型的结果进行比较。结果表明，与非平稳方法相比，平稳模型低估了滑坡概率。这是因为从研究区域的一个仪表中获取的当地降雨量的增加趋势，在平稳分析中没有考虑。因此，气候变化对滑坡的发生有着持续的影响。因此，为了反映气候变化的影响，在任何滑坡灾害分析中都应考虑能够应对气候变化的非平稳方法。