A hybrid dynamical–stochastic model of maximum temperature time series of Imphal, Northeast India incorporating nonlinear feedback and noise diagnostics

Climate variability is a complex phenomenon resulting from the interaction of numerous components within a climate system across a wide range of temporal and spatial scales. Although significant advances have been made in understanding global climate variability, there are relatively few studies on regional climate modeling, particularly in developing countries. Motivated by this research gap, we perform a systematic analysis of the variability of maximum temperatures recorded for a region called Imphal, which is the capital city of Manipur, located in Northeast India. We develop a data-driven framework for hybrid dynamical–stochastic modeling of maximum temperature data records spanning 73 years. A comprehensive, data-driven quantitative modeling of temperature dynamics for Imphal is not yet available in the literature. Our modeling approach integrates spectral decomposition, nonlinear feedback mechanisms, and stochastic noise characterization within a unified hybrid structure to investigate temperature variability over small and large time scales. Our data-driven approach yields key insights into the temperature dynamics, including a positive temperature increase in the region during the period investigated. Our hybrid model effectively reproduces the observed dynamics of maximum temperature variability of Imphal with high accuracy, validated by robust statistical and nonlinearity tests. The proposed framework offers practical implications for regional climate prediction and risk assessment for Imphal. Additionally, we provide derivations of Langevin and Fokker–Planck equations for the maximum temperature dynamics, offering the theoretical ground and analytical interpretation of the model that links the temperature dynamics with underlying physical principles. • Propose a data-driven model for analysis and prediction of regional maximum temperature variability. • This model, which incorporates nonlinear feedbacks and noise, is quite sensitive to the climate variability parameters. • The analysis of the temperature data is validated with statistical tests and nonlinear analysis. • Nonlinear regression analysis on the extrapolated data shows alarming future warming at regional scale. • The derived Langevin and Fokker–Planck equations for the maximum temperature provides the analytical interpretation of the model.