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

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 identify synoptic precipitation regimes and their climate-sensitive 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 (MJJA) 1980–2021) together with large-scale atmospheric covariates including zonal and meridional wind components, geopotential height, sea-level pressure, sea surface temperature, vertically integrated moisture flux and its divergence, and standard El Niño–Southern Oscillation (ENSO) indices: the Southern Oscillation Index (SOI), the Oceanic Niño Index (ONI), and the 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. The nHMM comprises p =29 284 estimated parameters fit to N _obs =2 386 692 daily observations (Nobs/p≈82), confirming adequate model identifiability. Mixed-effects inference on annual transition proportions reveals a statistically significant decline in wet-state self-persistence of ≈0.34 % yr ⁻¹ (95 % CI: [0.21, 0.47] % yr ⁻¹ ; ≈14.5 % over 1980–2022), coincident with rising GMSST. Out-of-sample ensembles for MJJA 2022 (100 daily members conditioned on Viterbi-decoded states) are well calibrated across all seven DTW zones: central 90 % prediction intervals achieve near-nominal coverage (88 %–98 %), asymmetry degrees ≤ 0.07, and widths that increase monotonically southward with climatological variance. GPD shape parameters range from ξ^<0 in anticyclonic dry states (bounded tails) to ξ^>0 in episodic frontal states (heavy tails), confirming state-specific extreme behaviour. The vine-copula layer yields statistically significant improvements over a conditionally independent baseline (Diebold–Mariano test with heteroskedasticity- and autocorrelation-consistent standard errors, p <0.001) in the rainfall-active southern zones, where skill scores of the Continuous Ranked Probability Score reach 0.51 (Zone 5) and 0.35 (Zone 6) relative to a climatological benchmark, and the area under the receiver operating characteristic curve exceeds 0.87 for the 25 mm exceedance threshold. 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, offering a principled pathway for regime-conditioned, uncertainty-aware precipitation prediction to support hydroclimate risk management in Chile and other topographically complex regions.