The changing pattern of climate variables has caused extreme weather events and severe disasters especially in mountainous regions. Such events have a detrimental impact on resources, environment and society. Thus, it has become imperative to examine trends and forecast of meteorological variables using scientific modeling approach at micro level. This study makes an attempt to examine trend in temperature and rainfall using Modified Mann–Kendall test and Sen’s slope estimator during 1980–2021. A Bagging-REPTree machine learning model was utilized for forecasting temperature and rainfall trend for the next 20 years (2022–2041) to understand the temporal dynamics in Shimla district of Indian Himalayan state. Correlation coefficient (R), mean squared error (MSE), mean absolute error (MAE), and root mean squared error mean (RMSE) performance were determined to assess effectiveness and precision of the model. The findings revealed that the frequency of intense rainfall in the district has increased, especially during the monsoon season (June–September) during 1980–2021. Annual maximum, minimum, and mean temperatures have exhibited significant variability while annual rainfall has shown a decreasing trend. The forecast analysis revealed significant trend for rainfall during monsoon season and increasing trend in the maximum temperature has been observed during summer and winter seasons. The analysis has provided sufficient evidence of variability and uncertainty in the behavior of meteorological variables. The outcome of the study may help in devising suitable adaptation and mitigation strategies to combat the effect of climate change in the hilly regions. The methodology adopted in the study may help in future progression of the research in different geographical regions of Western Himalayas.

The frequency and intensity of landslides have increased due to variability in precipitation and temperature across the globe. Assessment of landslide risk is essential for devising effective mitigation and making societies resilient. Through an in-depth literature review from 1996 to 2021, this study carried out descriptive, thematic, and mechanism analyses of the current literature to identify the gaps in the literature and suggest a way forward. Findings revealed a steady increase in the publication of landslide studies over the years. Most of the studies were conducted on a regional scale. Modeling, landslides, climate change, multi-hazards, and vulnerability figured as the prominent keywords in the reviewed articles. Slope, precipitation, land use/land cover, proximity to coastal areas, risk, and adaptation strategies were found as the dominant thematic variables. A large number of reviewed articles utilized statistical models. Uncertainty in landslide-climate modeling, lack of advanced and interactive models for predicting landslide susceptibility; and less attention to environmental and economic vulnerability were identified as the major research gaps. Effective early warning systems, timely forecasting for landslides and effective risk assessment are also scant in the existing literature. The current study proposed a comprehensive future framework for landslide risk assessment and implies timely and appropriate landslide mitigation based on climate scenario data, deterministic models, and policy beliefs. Strengthening preventive actions, preparedness as an early warning system, immediate response mechanism, landslide adaptation behavior, socio-economic vulnerability, and decision support systems are suggested for future research.