Frequency analysis of compound heat and wet extremes over Nepal

Nepal faces a spectrum of climate-related challenges, such as floods, landslides, and droughts, causing substantial economic and environmental consequences. The country’s vulnerability to climate change is intensified by its geographic features and inadequate preparedness, underscoring the significance of studying compound extremes for water resources management and disaster mitigation. The susceptibility to climate change, coupled with geographic vulnerability heightened by extreme weather events, like floods and landslides, poses significant threats to Nepal’s socioeconomic development, necessitating further research. Studying compound extremes is crucial due to the escalating disastrous effects of cascading disasters, impacting agriculture, food security, and water resources. This study utilizes observed and model data to analyze compound heatwaves and extreme precipitation events, categorizing extreme precipitation as values surpassing the 95^th percentile and defining heatwaves as three or more consecutive days with maximum temperatures exceeding the 95^th percentile. Station data from 1981-2020 along with CMIP6 13 models data from 1951-1982 for past event analysis and from 2015 to 2100 for future projections reveal varying trends in heatwaves, extreme precipitation, and compound events across Nepal. Different models indicate diverse distributions of heatwaves and wet extremes, with some regions experiencing a decline in heatwave events in the past. The sensitivity of compound events to lagging periods is evident, resulting in a shift from 15 to 30 days and a subsequent increase in compound events. SSP scenarios project an overall rise in compound heatwaves and extreme precipitation in the future, emphasizing the risk of cascading disasters and urging stakeholders and governments to implement robust disaster risk reduction and management strategies. The study underscores the complexities of extreme events in Nepal’s climate data and model results, stressing the importance of considering spatial, temporal, and modeling factors for effective climate change impact adaptation and mitigation.