S. Apipattanavis, G. Podestã, B. Rajagopalan, and R. Katz, A semiparametric multivariate and multisite weather generator, Water Resources Research, vol.34, issue.1, p.11401, 2007.
DOI : 10.1080/02626668909491307

URL : http://onlinelibrary.wiley.com/doi/10.1029/2006WR005714/pdf

G. Babu, A. Canty, and Y. Chaubey, Application of Bernstein Polynomials for smooth estimation of a distribution and density function, Journal of Statistical Planning and Inference, vol.105, issue.2, pp.377-392, 2002.
DOI : 10.1016/S0378-3758(01)00265-8

S. Bernstein, Démonstration du théorème de Weierstrass fondée sur le calcul des probabilités, Communications de la Société mathématique de Kharkow, vol.13, issue.1, pp.1-2, 1912.

T. Bouezmarni and J. Rolin, Bernstein estimator for unbounded density function, Journal of Nonparametric Statistics, vol.9, issue.3, pp.145-161, 2007.
DOI : 10.1007/978-1-4899-3324-9

J. Carreau and Y. Bengio, A hybrid Pareto model for asymmetric fat-tailed data: the univariate case, Extremes, vol.15, issue.1, pp.53-76, 2008.
DOI : 10.1007/978-3-642-33483-2

J. Carreau, P. Naveau, and L. Neppel, Partitioning into hazard subregions for regional peaks-over-threshold modeling of heavy precipitation, Water Resources Research, vol.26, issue.3, pp.4407-4426, 2017.
DOI : 10.1029/2006WR005095

S. Coles, An introduction to statistical modeling of extreme values, 2001.
DOI : 10.1007/978-1-4471-3675-0

A. Davison, Modelling Excesses over High Thresholds, with an Application, Statistical Extremes and Applications, pp.461-482, 1984.
DOI : 10.1007/978-94-017-3069-3_34

G. Evin, J. Blanchet, E. Paquet, F. Garavaglia, and D. Penot, A regional model for extreme rainfall based on weather patterns subsampling, Journal of Hydrology, vol.541, pp.1185-1198, 2016.
DOI : 10.1016/j.jhydrol.2016.08.024

G. Evin, A. Favre, and B. Hingray, Stochastic generation of multi-site daily precipitation focusing on extreme events, Hydrology and Earth System Sciences, vol.22, issue.1, pp.655-672, 2018.
DOI : 10.1029/2002WR001769

R. Farouki, The Bernstein polynomial basis: A centennial retrospective, Computer Aided Geometric Design, vol.29, issue.6, pp.379-419
DOI : 10.1016/j.cagd.2012.03.001

E. Furrer and R. Katz, Improving the simulation of extreme precipitation events by stochastic weather generators, Water Resources Research, vol.83, issue.3, p.12439, 2008.
DOI : 10.1007/s00704-005-0156-x

F. Garavaglia, J. Gailhard, E. Paquet, M. Lang, R. Garcon et al., Introducing a rainfall compound distribution model based on weather patterns sub-sampling, Hydrology and Earth System Sciences, vol.14, issue.6, pp.951-964, 2010.
DOI : 10.5194/hess-14-951-2010

URL : https://hal.archives-ouvertes.fr/hal-00494939

S. Ghosal, polynomials, The Annals of Statistics, vol.29, issue.5, pp.1264-1280, 2001.
DOI : 10.1214/aos/1013203453

S. Grimshaw, Computing Maximum Likelihood Estimates for the Generalized Pareto Distribution, Technometrics, vol.4, issue.2, pp.185-191, 1993.
DOI : 10.1214/ss/1177012400

G. Hegerl and F. Zwiers, Use of models in detection and attribution of climate change, Wiley interdisciplinary reviews: climate change, pp.570-591, 2011.
DOI : 10.1038/453296a

Y. Ji, C. Wu, P. Liu, J. Wang, and K. Coombes, Applications of beta-mixture models in bioinformatics, Bioinformatics, vol.6, issue.7, pp.2118-2122, 2005.
DOI : 10.1214/aos/1176344136

Y. Kakizawa, Bernstein polynomial probability density estimation, Journal of Nonparametric Statistics, vol.2, issue.5, pp.709-729, 2004.
DOI : 10.1007/BF02056918

R. Katz, Precipitation as a Chain-Dependent Process, Journal of Applied Meteorology, vol.16, issue.7, pp.671-676, 1977.
DOI : 10.1175/1520-0450(1977)016<0671:PAACDP>2.0.CO;2

URL : http://journals.ametsoc.org/doi/pdf/10.1175/1520-0450%281977%29016%3C0671%3APAACDP%3E2.0.CO%3B2

R. Katz, M. Parlange, and P. Naveau, Statistics of extremes in hydrology, Advances in Water Resources, vol.25, issue.8-12, pp.1287-1304, 2002.
DOI : 10.1016/S0309-1708(02)00056-8

A. Leblanc, A bias-reduced approach to density estimation using Bernstein polynomials, Journal of Nonparametric Statistics, vol.2, issue.4, pp.459-475, 2010.
DOI : 10.1007/978-1-4899-4493-1

A. Leblanc, On estimating distribution functions using Bernstein polynomials, Annals of the Institute of Statistical Mathematics, vol.26, issue.5, pp.919-943
DOI : 10.1016/B978-0-12-568002-8.50011-2

A. Leblanc, On the boundary properties of Bernstein polynomial estimators of density and distribution functions, Journal of Statistical Planning and Inference, vol.142, issue.10, pp.2762-2778
DOI : 10.1016/j.jspi.2012.03.016

A. Macdonald, C. Scarrott, D. Lee, B. Darlow, M. Reale et al., A flexible extreme value mixture model, Computational Statistics & Data Analysis, vol.55, issue.6, pp.2137-2157, 2011.
DOI : 10.1016/j.csda.2011.01.005

G. Mclachlan and T. Krishnan, The EM algorithm and extensions, 2007.

S. Nadarajah, Extremes of Daily Rainfall in West Central Florida, Climatic Change, vol.22, issue.2-3, pp.325-342, 2005.
DOI : 10.1007/s10584-005-1812-y

P. Naveau, R. Huser, P. Ribereau, and A. Hannart, Modeling jointly low, moderate, and heavy rainfall intensities without a threshold selection, Water Resources Research, vol.34, issue.3, pp.2753-2769, 2016.
DOI : 10.3354/cr00696

URL : http://repository.kaust.edu.sa/kaust/bitstream/10754/608588/1/Naveau_et_al-2016-Water_Resources_Research.pdf

S. Petrone, Bayesian density estimation using bernstein polynomials, Canadian Journal of Statistics, vol.2, issue.3, pp.105-126, 1999.
DOI : 10.1016/B978-0-12-568002-8.50011-2

P. Ribereau, P. Naveau, and A. Guillou, A note of caution when interpreting parameters of the distribution of excesses, Advances in Water Resources, vol.34, issue.10, pp.1215-1221, 2011.
DOI : 10.1016/j.advwatres.2011.05.003

C. Richardson, Stochastic simulation of daily precipitation, temperature, and solar radiation, Water Resources Research, vol.8, issue.1, pp.182-190, 1981.
DOI : 10.1175/1520-0450(1979)018<0034:MLEOFC>2.0.CO;2

R. Stern and R. Coe, A Model Fitting Analysis of Daily Rainfall Data, Journal of the Royal Statistical Society. Series A (General), vol.147, issue.1, p.1, 1984.
DOI : 10.2307/2981736

R. Vitale, A Bernstein polynomial approach to density function estimation. Statistical inference and related topics 2, pp.87-99, 1975.
DOI : 10.1016/b978-0-12-568002-8.50011-2

M. Vrac and P. Naveau, Stochastic downscaling of precipitation: From dry events to heavy rainfalls, Water Resources Research, vol.12, issue.213, pp.1-13, 2007.
DOI : 10.1175/1520-0442(1999)012<2474:TAMAAS>2.0.CO;2

M. Vrac, M. Stein, and K. Hayhoe, Statistical downscaling of precipitation through nonhomogeneous stochastic weather typing, Climate Research, vol.34, issue.3, pp.169-184, 2007.
DOI : 10.3354/cr00696

D. Wilks, Conditioning stochastic daily precipitation models on total monthly precipitation, Water Resources Research, vol.18, issue.6, pp.1429-1439, 1989.
DOI : 10.1029/WR018i005p01461

D. Wilks, Interannual variability and extreme-value characteristics of several stochastic daily precipitation models, Agricultural and Forest Meteorology, vol.93, issue.3, pp.153-169, 1999.
DOI : 10.1016/S0168-1923(98)00125-7

D. Wilks, Statistical methods in the atmospheric sciences, 2011.

D. Woolhiser and G. Pegram, Maximum Likelihood Estimation of Fourier Coefficients to Describe Seasonal Variations of Parameters in Stochastic Daily Precipitation Models, Journal of Applied Meteorology, vol.18, issue.1, pp.34-42, 1979.
DOI : 10.1175/1520-0450(1979)018<0034:MLEOFC>2.0.CO;2

W. Zucchini and P. Adamson, The occurrence and severity of droughts in South Africa, 1984.