Articles | Volume 30, issue 20
https://doi.org/10.5194/hess-30-6531-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-30-6531-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
23 Oct 2026
Research article |
| 23 Oct 2026
| 23 Oct 2026
Characterizing Climate-Driven Shifts in Chilean Rainfall Regimes with a Hybrid Hidden Markov–Copula Framework
Research Center on Sustainability and Strategic Resource Management (CISGER), Faculty of Engineering, Universidad del Desarrollo (UDD), Avda. Plaza 680, Santiago, Chile
Francisca Kleisinger
Research Center on Sustainability and Strategic Resource Management (CISGER), Faculty of Engineering, Universidad del Desarrollo (UDD), Avda. Plaza 680, Santiago, Chile
Diego Rivera
Research Center on Sustainability and Strategic Resource Management (CISGER), Faculty of Engineering, Universidad del Desarrollo (UDD), Avda. Plaza 680, Santiago, Chile
Andrés Wilson
Research Center on Sustainability and Strategic Resource Management (CISGER), Faculty of Engineering, Universidad del Desarrollo (UDD), Avda. Plaza 680, Santiago, Chile
Alex Godoy-Faundez
Research Center on Sustainability and Strategic Resource Management (CISGER), Faculty of Engineering, Universidad del Desarrollo (UDD), Avda. Plaza 680, Santiago, Chile
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Cited articles
Aas, K., Czado, C., Frigessi, A., and Bakken, H.: Pair-copula constructions of multiple dependence, Insurance: Mathematics and Economics, 44, 182–198, 2009. a
Ailliot, P., Thompson, C., and Thomson, P.: Space–Time Modeling of Precipitation Using a Hidden Markov Model and Censored Gaussian Distributions, J. Hydrol., 365, 56–73, https://doi.org/10.1016/j.jhydrol.2008.11.027, 2009. a, b
Arias, P. A., Garreaud, R., Poveda, G., Espinoza, J. C., Molina-Carpio, J., Masiokas, M., Viale, M., Scaff, L., and Van Oevelen, P. J.: Hydroclimate of the Andes part II: Hydroclimate variability and sub-continental patterns, Front. Earth Sci., 8, 505467,https://doi.org/10.3389/feart.2020.505467, 2021. a, b
Barrett, B. S. and Hameed, S.: Seasonal variability in precipitation in central and southern Chile: Modulation by the South Pacific high, J. Clim., 30, 55–69, 2017. a
Barría, P., Rojas, M., Moraga, P., Muñoz, A., Bozkurt, D., and Alvarez-Garreton, C.: Anthropocene and streamflow: Long-term perspective of streamflow variability and water rights, Elem. Sci. Anth., 7, 2, https://doi.org/10.1525/elementa.340., 2019. a
Becker, E. and Schmitz, G.: Climatological effects of orography and land–sea heating contrasts on the gravity wave–driven circulation of the mesosphere, J. Atmos. Sci., 60, 103–118, https://doi.org/10.1175/1520-0469(2003)060<0103:CEOOAL>2.0.CO;2, 2003. a
Bellone, E., Hughes, J. P., and Guttorp, P.: A Hidden Markov Model for Downscaling Synoptic Atmospheric Patterns to Precipitation Amounts, Clim. Res., 15, 1–12, https://doi.org/10.3354/cr015001, 2000. a
Bergmeir, C. and Benítez, J. M.: On the use of cross-validation for time series predictor evaluation, Inform. Sciences, 191, 192–213, https://doi.org/10.1016/j.ins.2011.12.028, 2012. a
Boisier, J. P.: CR2MET v2.5: High-resolution precipitation and temperature dataset for 1960–2021 in continental Chile, Zenodo [data set], https://doi.org/10.5281/zenodo.7529682, 2023. a, b
Boisier, J. P., Alvarez-Garretón, C., Cepeda, J., Osses, A., Vásquez, N., and Rondanelli, R.: CR2MET A high-resolution precipitation and temperature dataset for hydroclimatic research in Chile,Geophysical Research Abstracts, 20, EGU2018-19739, EGU General Assembly 2018, https://meetingorganizer.copernicus.org/EGU2018/EGU2018-19739.pdf. (last access: 21 October 2026), 2018. a
Boisier, J. P., Alvarez-Garreton, C., Cordero, R. R., Damiani, A., Gallardo, L., Garreaud, R. D., Lambert, F., Ramallo, C., Rojas, M., and Rondanelli, R.: Anthropogenic drying in central-southern Chile evidenced by long-term observations and climate model simulations, Elementa, 6, https://doi.org/10.1525/elementa.328, 2018. a
Bozkurt, D., Rondanelli, R., Gonzalez-Reyes, A., Garreaud, R. D., Muñoz, A., Flores-Aqueveque, V., Aguilera, D., Farias-Barahona, D., Lambert, F., and Ramallo, C.: Climate change impacts on water availability and agriculture in Central Chile, Int. J. Climatol., 41, 1–21, https://doi.org/10.1002/joc.6789, 2021. a
Burger, F., Brock, B., and Montecinos, A.: Seasonal and elevational contrasts in temperature trends in Central Chile between 1979 and 2015, Glob. Planet. Change, 162, 136–147, 2018. a
Bárdossy, A. and Pegram, G.: Copula-Based Multivariate Distribution Functions for Hydrological Modeling, Water Resour. Res., 46, W07536, https://doi.org/10.1029/2009WR007857, 2010. a, b, c
Cavalleri, F., Lussana, C., Viterbo, F., Brunetti, M., Bonanno, R., Manara, V., Lacavalla, M., Sperati, S., Raffa, M., Capecchi, V., Cesari, D., Giordani, A., Cerenzia, I. M. L., and Maugeri, M.: Multi-scale assessment of high-resolution reanalysis precipitation fields over Italy, Atmos. Res., 312, 107734, https://doi.org/10.1016/j.atmosres.2024.107734, 2024. a
Charles, S. P., Timbal, B., and Nguyen, K.: Statistical Downscaling for Assessing Simulated Precipitation over Western Victoria, Australia, Int. J. Climatol., 34, 719–732, https://doi.org/10.1002/joc.3727, 2014. a
Cioffi, F., Conticello, F. R., and Lall, U.: Stochastic Scenarios for 21st Century Rainfall Seasonality, Daily Frequency, and Intensity in South Florida, J. Water Res. Pl., 146, 04020058, https://doi.org/10.1061/(ASCE)WR.1943-5452.0001250, 2020. a
CR2: Centro de Ciencia del Clima y la Resiliencia, Datos de Precipitación, https://www.cr2.cl/datos-de-precipitacion/ (last access: 29 April 2024), 2024. a
Dawid, A. P.: Present Position and Potential Developments: Some Personal Views: Statistical Theory: The Prequential Approach, J. Roy. Stat. Soc. Ser. A, 147, 278–292, https://doi.org/10.2307/2981683, 1984. a
Diebold, F. X. and Mariano, R. S.: Comparing Predictive Accuracy, J. Bus. Econ. Stat., 13, 253–263, https://doi.org/10.1080/07350015.1995.10524599, 1995. a
Franzke, C. L. E. and O'Kane, T. J. (eds.): Nonlinear and Stochastic Climate Dynamics, Cambridge University Press, https://doi.org/10.1017/9781316339251, 2017. a, b, c
Garreaud, R., Alvarez-Garreton, C., Barichivich, J., Boisier, J., Christie, D., Galleguillos, M., LeQuesne, C., McPhee, J., and Zambrano-Bigiarini, M.: The 2010–2015 megadrought in central Chile: impacts on regional hydroclimate and vegetation, Hydrol. Earth Syst. Sci., 21, 6307–6327, https://doi.org/10.5194/hess-21-6307-2017, 2017. a, b
Garreaud, R. D., Boisier, J. P., Rondanelli, R., Montecinos, A., Sepúlveda, H. H., and Veloso-Aguila, D.: The Central Chile Mega Drought (2010–2018): A climate dynamics perspective, Int. J. Climatol., 40, 421–439, https://doi.org/10.1002/joc.6219, 2020. a, b
Genest, C. and MacKay, J.: A primer on the copula approach to modeling dependence between random variables, Stat. Sci., 7, 113–122, 1986. a
Genest, C. and Rémillard, B.: Validity of the Parametric Bootstrap for Goodness-of-Fit Testing in Semiparametric Models, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 44, 1096–1127, https://doi.org/10.1214/07-AIHP148, 2008. a
Genest, C., Rémillard, B., and Beaudoin, D.: Goodness-of-Fit Tests for Copulas: A Review and a Power Study, Insurance: Mathematics and Economics, 44, 199–213, https://doi.org/10.1016/j.insmatheco.2007.10.005, 2009. a
Giorgino, T.: Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package, J. Stat. Softw., 31, 1–24, https://doi.org/10.18637/jss.v031.i07, 2009. a
Gneiting, T. and Raftery, A. E.: Strictly Proper Scoring Rules, Prediction, and Estimation, J. Am. Stat. Assoc., 102, 359–378, https://doi.org/10.1198/016214506000001437, 2007. a, b, c
Herrera, M., Kleisinger, F., and Wilsón, A.: Spatiotemporal Forecasting in Climate Data Using EOFs and Machine Learning Models: A Case Study in Chile, arXiv [preprint], https://arxiv.org/abs/2502.17495 (last access: 20 October 2026), 2025. a
Herrera-Marín, M.: Hybrid Hidden Markov–Copula Precipitation Framework, Zenodo [code], https://doi.org/10.5281/zenodo.23322615, 2026. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), https://doi.org/10.24381/cds.adbb2d47, 2023. a, b
Hoskins, B. J. and Hodges, K. I.: A new perspective on Southern Hemisphere storm tracks, J. Clim., 18, 4108–4129, 2005. a
Jordan, A., Krüger, F., and Lerch, S.: Evaluating Probabilistic Forecasts with scoringRules, J. Stat. Softw., 90, 1–37, https://doi.org/10.18637/jss.v090.i12, 2019. a
Khalil, A. F., Kwon, H.-H., Lall, U., and Kaheil, Y. H.: Predictive Downscaling Based on Non-Homogeneous Hidden Markov Models, Hydrol. Sci. J., 55, 333–350, https://doi.org/10.1080/02626661003780342, 2010. a, b
Latoja, D., Lillo-Saavedra, M., Gonzalo-Martin, C., Godoy-Faúndez, A., Somos-Valenzuela, M., and Rivera, D.: Decadal Variability of Dry Days in Central Chile, Earth Systems and Environment, 9, 1117–1134, https://doi.org/10.1007/s41748-024-00526-6, 2025. a, b
Mayer, J., Mayer, M., and Haimberger, L.: Consistency and Homogeneity of Atmospheric Energy, Moisture, and Mass Budgets in ERA5, J. Clim., 34, 3955–3974, https://doi.org/10.1175/JCLI-D-20-0676.1, 2021. a
McLachlan, G. and Krishnan, T.: The EM Algorithm and Extensions, vol. 382, John Wiley & Sons, Hoboken, NJ, 2nd edn., https://doi.org/10.1002/9780470191613, 2008. a
Montecinos, A., Kurgansky, M. V., Muñoz, C., and Takahashi, K.: Non-ENSO interannual rainfall variability in central Chile during austral winter, Theor. Appl. Climatol., 106, 557–568, https://doi.org/10.1007/s00704-011-0457-1, 2011. a, b
Montero, P. and Vilar, J. A.: TSclust: An R package for time series clustering, J. Stat. Softw., 62, 1–43, https://doi.org/10.18637/jss.v062.i01, 2015. a, b
Nelsen, R. B.: An Introduction to Copulas, Springer Series in Statistics, Springer, New York, 2 edn., https://doi.org/10.1007/0-387-28678-0, 2006. a
Pablo Sarricolea, M. H.-O. and Meseguer-Ruiz, Ó.: Climatic regionalisation of continental Chile, J. Maps, 13, 66–73, https://doi.org/10.1080/17445647.2016.1259592, 2017. a
Pica-Téllez, A., Garreaud, R., Meza, F., Bustos, S., Falvey, M., Ibarra, M., Duarte, K., Ormazábal, R., Dittborn, R., and Silva, I.: Informe proyecto arclim: Atlas de riesgos climáticos para chile, Tech. rep., Centro de Ciencia del Clima y la Resiliencia, Centro de Cambio Global UC y Meteodata para el Ministerio del Medio Ambiente a través de la Deutsche Gesellschaft für Internationale Zusammenarbeit (giz), Santiago, Chile, Ministerio del Medio Ambiente, Gobierno de Chile, https://arclim.mma.gob.cl/media/informes_consolidados/Informe_ARCLIM_Consolidado_Final.pdf (last access: 21 October 2026), 2020. a
Politis, D. N. and Romano, J. P.: The Stationary Bootstrap, J. Am. Stat. Assoc., 89, 1303–1313, https://doi.org/10.1080/01621459.1994.10476870, 1994. a
Pyper, B. J. and Peterman, R. M.: Comparison of methods to account for autocorrelation in correlation analyses of fish data, Can. J. Fish. Aquat. Sci., 55, 2127–2140, https://doi.org/10.1139/f98-104, 1998. a
R Core Team: R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (last access: 20 October 2026), 2023. a
Robertson, A. W., Kirshner, S., and Smyth, P.: Downscaling of Daily Rainfall Occurrence over Northeast Brazil Using a Hidden Markov Model, J. Climate, 17, 4407–4424, https://doi.org/10.1175/JCLI-3216.1, 2004. a, b, c
Robertson, A. W., Kirshner, S., Smyth, P., Charles, S. P., and Bates, B. C.: Subseasonal-to-interdecadal variability of the Australian monsoon over North Queensland, Q. J. Roy. Meteorol. Soc., 132, 519–542, 2006. a
Rosenblatt, M.: Remarks on a Multivariate Transformation, Ann. Math. Stat., 23, 470–472, https://doi.org/10.1214/aoms/1177729394, 1952. a
Schepsmeier, U., Stöber, J., Brechmann, E. C., Graeler, B., Nagler, T., Erhardt, T., Almeida, C., Min, A., and Ruckdeschel, P.: Package “VineCopula”, R Package Version 1.6.0, https://doi.org/10.32614/CRAN.package.VineCopula, 2015. a, b, c
Scheuerer, M. and Hamill, T. M.: Variogram-based proper scoring rules for probabilistic forecasts of multivariate quantities, Mon. Weather Rev., 143, 1321–1334, https://doi.org/10.1175/MWR-D-14-00269.1, 2015. a
Stephenson, A. G.: evd: Extreme Value Distributions, R News, 2, 31–32, https://journal.r-project.org/articles/RN-2002-015/ (last access: 20 October 2026), 2002. a
Székely, G. J. and Rizzo, M. L.: Energy Statistics: A Class of Statistics Based on Distances, Stat. Sci., 28, 558–575, https://doi.org/10.1214/13-STS437, 2013. a
Valdés-Pineda, R., Valdes, J. B., Diaz, H. F., and Pizarro-Tapia, R.: Analysis of spatio-temporal changes in annual and seasonal precipitation variability in South America-Chile and related ocean–atmosphere circulation patterns, Int. J. Climatol., 36, 2979–3001, 2016. a
Vrac, M. and Naveau, P.: Stochastic downscaling of precipitation: From dry events to heavy rainfalls, Water Resour. Res., 43, W07402, https://doi.org/10.1029/2006WR005308, 2007. a
Vrac, M. and Friederichs, P.: Multivariate—Intervariable, Spatial, and Temporal—Bias Correction, J. Climate, 28, 218–237, https://doi.org/10.1175/JCLI-D-14-00059.1, 2015. a, b, c
Vuong, Q. H.: Likelihood Ratio Tests for Model Selection and Non-Nested Hypotheses, Econometrica, 57, 307–333, https://doi.org/10.2307/1912557, 1989. a
Ward, J. H.: Hierarchical Grouping to Optimize an Objective Function, J. Am. Stat. Assoc., 58, 236–244, https://doi.org/10.1080/01621459.1963.10500845, 1963. a
Wilks, D. S.: Statistical Methods in the Atmospheric Sciences, Academic Press, 3rd edn., ISBN 978-0-12-385023-2, 2011. a
Zucchini, W., MacDonald, I. L., and Langrock, R.: Hidden Markov Models for Time Series: An Introduction Using R, Chapman & Hall/CRC, 2nd edn., https://doi.org/10.1201/b20790, 2016. a
Short summary
Chile’s changing rainfall makes water planning more difficult. Using decades of rainfall observations and climate records, we identified five recurring weather patterns and simulated rainfall across the country. We found that wet weather patterns have become less persistent over four decades, alongside rising ocean temperatures. Tests against observations from 2022 showed that the simulations captured rainfall uncertainty well. These results can help assess drought and flood risks.
Chile’s changing rainfall makes water planning more difficult. Using decades of rainfall...
