Statistical modelling of century-long precipitation and temperature extremes in Himachal Pradesh, India: generalized extreme value approach and return level estimation

Climate variability, particularly, that of temperature and precipitation, has received a great deal of attention worldwide. In the present work, we have carried out a thorough statistical modelling of precipitation and temperature extremes by taking century-long (1901–2002) datasets into consideration for Himachal Pradesh, India. Globally, the patterns of extreme weather events have changed from longer and hotter heat waves to heavier rains. A fragile ecosphere, climatic change, and the unique geology of Himachal Pradesh have all led to a sharp increase in natural disasters in the hill state over time. The climate of the state varies by altitude. Since precipitation and temperature are the main driving forces behind climate change, quantifying these two variables over a range of return periods is crucial in policymaking for minimizing the potential harm brought on by variations in these atmospheric elements. The statistical modelling of the meteorological data and probabilistic forecasting were done using extreme value theory (EVT), an advanced statistical modelling approach that is widely acknowledged. The generalized extreme value (GEV) analysis approach of EVT is utilized for the probabilistic forecasting of the extremities. The extreme values and their probable occurrence are estimated once every 50, 80, 100, 120, 200, 250, 300, and 500 years, respectively. By estimating the probabilities of extreme events, we can better understand the potential risks and impacts of climate change and develop strategies to manage and mitigate these risks.