The mountainous areas are vulnerable to climate change and may have many socio-economic and environmental implications. The changing pattern of meteorological variables has deleterious effects on natural resources and livelihood. This paper makes an attempt to analyse trend and forecast metrological variables in Nainital district of India. Monthly, seasonal, and annual trends in rainfall and temperature were examined by Modified Mann–Kendall during 1989–2019. The magnitude of trend in temperature and rainfall was determined using Sen's slope estimator. Ensemble machine learning model was utilized for forecasting the variables for the next 16 years (2020–2035). The effectiveness of the model was examined through statistical performance assessors. The results revealed a significant increasing trend in the rainfall (at the rate of 9.42 mm/year) during 1989–2019. Increasing trend in the mean, minimum, and maximum temperatures on an annual basis was observed in the district. A remarkable increase in the rainfall and temperature was forecasted during various seasons. The findings of the study may help the stakeholders in devising suitable adaptation measures to climate variability. The bagging approach has shown its effectiveness in forecasting meteorological variables. The other geographical regions may find the methodology effective for analyzing climate variability and lessening its impact.

山区容易受到气候变化的影响，并可能产生许多社会经济和环境影响。气象变量的变化模式对自然资源和生计产生有害影响。本文尝试对印度奈尼塔尔地区的计量变量进行趋势分析和预测。Modified Mann-Kendall 对 1989 年至 2019 年期间降雨量和温度的月度、季节和年度趋势进行了研究。温度和降雨量趋势的大小是使用森斜率估计器确定的。利用集成机器学习模型来预测未来 16 年（2020-2035 年）的变量。该模型的有效性通过统计绩效评估人员进行了检查。结果显示，1989年至2019年期间降雨量呈显着增加趋势（每年9.42毫米）。该地区的年平均、最低和最高气温呈上升趋势。预计各个季节的降雨量和气温都会显着增加。研究结果可能有助于利益相关者制定适当的气候变化适应措施。bagging 方法已显示出其在预测气象变量方面的有效性。其他地理区域可能会发现该方法可有效分析气候变化并减轻其影响。