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The Western Ghats mountain range holds critical importance in regulatingmonsoon rainfall across Southern India, with a profound impact on regionalagriculture. Here, we analyze daily wet-day rainfall data for the monsoonmonths between 1901-2022 for the Northern, Middle, and Southern Western Ghatsregions. Motivated by an exploratory data analysis, we introduce asemiparametric Bayesian generalized exponential (GE) regression model; despitethe underlying GE distribution assumption being well-known in the literature,including in the context of rainfall analysis, no research explored it in aregression setting, as of our knowledge. Our proposed approach involvesmodeling the GE rate parameter within a generalized additive model framework.An important feature is the integration of a principled distance-based priorfor the GE shape parameter; this allows the model to shrink to an exponentialregression model that retains the advantages of the exponential family. We drawinferences using the Markov chain Monte Carlo algorithm. Extensive simulationsdemonstrate that the proposed model outperforms simpler alternatives. Applyingthe model to analyze the rainfall data over 122 years provides insights intomodel parameters, temporal patterns, and the impact of climate change. Weobserve a significant decreasing trend in wet-day rainfall for the SouthernWestern Ghats region.

A Semiparametric Generalized Exponential Regression Model with a Principled Distance-based Prior for Analyzing Trends in Rainfall