Characterizing Climate-Driven Shifts in Chilean Rainfall Regimes with a Hybrid Hidden Markov–Copula Framework

Abstract. Chile's hydroclimate exhibits pronounced meridional gradients and strong interannual variability, posing persistent challenges for regime-aware, probabilistic rainfall prediction. We introduce a hierarchical framework that explicitly separates large-scale regime dynamics from local spatial dependence. The approach integrates: (i) a covariate-driven non-homogeneous Hidden Markov Model (nHMM) to learn synoptic precipitation regimes and their transitions; (ii) Dynamic Time Warping (DTW) clustering to delineate precipitation-coherent climatic zones; and (iii) state-conditional Regular Vine copulas with Generalized Pareto (GPD) tails to model residual spatial dependence and extremes. The analysis employs the 0.05° daily CR2MET precipitation product over continental Chile (462 grid points, May–August 1980–2021) together with large-scale atmospheric covariates including the Southern Oscillation Index (SOI), the Oceanic Niño Index (ONI), and Global Mean Sea-Surface Temperature (GMSST).

Five physically consistent rainfall regimes emerge, spanning from an anticyclonic dry state to a cyclonic wet state, confirmed by composites of mean sea-level pressure, 850-hPa winds, and 500-hPa geopotential height. Mixed-effects inference on the transition matrix reveals a statistically significant decline in wet-state persistence of ~0.34 % yr^-1 (≈14.5 % over 1980–2022), coincident with rising GMSST. Out-of-sample ensembles for 2022 (100 daily members conditioned on Viterbi states) are well calibrated: central 90 % prediction intervals achieve near-nominal coverage, low asymmetry, and widths increasing southward with climatological variance.

By disentangling regime timing and drivers from residual spatial co-variability and extremes, the proposed nHMM – DTW – vine – GPD framework yields meteorologically coherent states, spatially consistent probabilistic simulations, and quantitatively validated forecasts. The method is computationally tractable and transferable, offering a principled pathway for regime-conditioned, uncertainty-aware precipitation prediction to support hydroclimate risk management in Chile and other topographically complex regions.