In this study, temporal trend analysis was conducted on the annual and quarterly meteorological variables of Lanzhou from 1951 to 2016, and a weighted Markov model for extremely high temperature prediction was constructed. Several non-parametric methods were used to analyse the trend of meteorological variables. Considering that sequence autocorrelation may affect the accuracy of the trend test, we performed an autocorrelation test and carried out trend analysis for sequences with autocorrelation after removing correlation. The results show that the maximum temperature, minimum temperature and average temperature in Lanzhou all have a significant upward trend and show different performances in each season. In detail, the trend of maximum temperature in the summer is not significant, while the upward trend of minimum temperature in the winter is the most significant, which leads to more and more “warm winter” phenomenon. Finally, we construct a weighted Markov prediction model for extremely high temperature and obtain the conclusion that the prediction results by the model are consistent with the actual situation.

本研究对1951年至2016年兰州的年度和季度气象变量进行了时空趋势分析，并建立了加权马尔可夫模型进行极高温预报。几种非参数方法被用来分析气象变量的趋势。考虑到序列自相关可能会影响趋势测试的准确性，因此我们在删除相关性之后执行了自相关测试，并对具有自相关序列进行了趋势分析。结果表明，兰州的最高气温，最低气温和平均气温都有明显的上升趋势，并且每个季节表现不同。详细而言，夏季最高温度的趋势并不明显，而冬季最低温度的上升趋势最为明显，这导致越来越多的“暖冬”现象。最后，我们建立了一个极高温度的加权马尔可夫预测模型，并得出结论：该模型的预测结果与实际情况相吻合。