. February-?, ) ?(I?) -0.01(-0.29,-0.01) -0

. October-?, (I?) -0

. Tmax-1, 02,-1.74]) -1.77([-1.80,-1.57]) -0

. Tmin-1, 16]) -1.76([-2.27,-1.96]) -0

. February-?, ) ?(I?) -0.13(-0.41,-0.08) -0

P. Alaton, B. Djehiche, and D. Stillberger, On modelling and pricing weather derivatives, Applied Mathematical Finance, vol.9, issue.1, 2002.
DOI : 10.1093/0198775180.001.0001

J. M. Albin, On Extremal Theory for Stationary Processes, The Annals of Probability, vol.18, issue.1, pp.92-128, 1990.
DOI : 10.1214/aop/1176990940

L. V. Alexander, Global observed changes in daily climate extremes of temperature and precipitation, Journal of Geophysical Research, vol.18, issue.20, 2006.
DOI : 10.1029/2005JD006290

K. Tank, A. M. Knnen, and G. P. , Trends in Indices of Daily Temperature and Precipitation Extremes in Europe, 1946???99, Journal of Climate, vol.16, issue.22, pp.3665-3680, 2003.
DOI : 10.1175/1520-0442(2003)016<3665:TIIODT>2.0.CO;2

C. W. Anderson and K. F. Turkman, Continuous-and discrete-time extremes : some theoretical and practical aspects. under preparation, 2008.

J. Aza¨?saza¨?s and M. Wschebor, R??gularit?? de la loi du maximum de processus gaussiens r??guliers, Comptes Rendus de l'Acad??mie des Sciences - Series I - Mathematics, vol.328, issue.4, pp.333-336, 1999.
DOI : 10.1016/S0764-4442(99)80220-1

S. M. Barbosa, Changing seasonality in Europe???s air temperature, The European Physical Journal Special Topics, vol.174, issue.1, pp.81-89, 2009.
DOI : 10.1140/epjst/e2009-01091-9

S. M. Berman, Extreme Sojourns of Diffusion Processes, The Annals of Probability, vol.16, issue.1, pp.361-374, 1988.
DOI : 10.1214/aop/1176991908

S. M. Berman, Sojourns and extremes of stochastic processes. Waddsworth & Brooks/Cole statistics/probability series, 1992.

P. J. Bickel and Y. Ritov, Testing for goodness of fit : a new approach, Nonparametric statistics and related topics, pp.51-57, 1992.

D. Brody, J. Syroka, and M. Zervos, Dynamical pricing of weather derivatives, Quantitative Finance, vol.55, issue.3, pp.189-198, 2002.
DOI : 10.1175/1520-0477(2000)081<2075:WDAWIC>2.3.CO;2

L. D. Brown and M. Levine, Variance estimation in nonparametric regression via the difference sequence method, The Annals of Statistics, vol.35, issue.5, pp.2219-2232, 2007.
DOI : 10.1214/009053607000000145

S. D. Campbell and F. X. Diebold, Weather forecasting for weather derivatives, Journal of the American Statistical Association, vol.100, 2002.

M. Cao, A. Li, and J. Wei, Precipitation modelling and contract valuation : A frontier in weather derivatives, The Journal of Alternative Investments, 2004.

M. Cao and J. Wei, Pricing weather derivatives : an equilibrium approach . Working paper, 2000.

V. Chavez-demoulin, Two problems in environmental statistics : capture-recapture analysis and smooth extremal models, 1999.

V. Chavez-demoulin and A. C. Davison, Generalized additive models for sample extremes, Appl. Statist, vol.54, pp.207-222, 2005.

M. Chen and H. An, The probabilistic properties of the nonlinear autoregressive model with conditional heteroskedasticity, Acta Mathematicae applicatae sinica, vol.15, 1999.

C. K. Chu and J. S. Marron, Comparison of Two Bandwidth Selectors with Dependent Errors, The Annals of Statistics, vol.19, issue.4, pp.1906-1918, 1991.
DOI : 10.1214/aos/1176348377

W. S. Cleveland, Robust Locally Weighted Regression and Smoothing Scatterplots, Journal of the American Statistical Association, vol.39, issue.368, pp.829-836, 1979.
DOI : 10.1214/aos/1176343886

S. Coles, An Introduction to Statistical Modelling of Extreme Values, 2001.

I. Cordery, R. Mehrotra, and M. J. Nazemosadat, How reliable are standard indicators of stationarity ? Stoch, Environ. Res. Risk Assess, vol.21, p.765771, 2007.

A. Cowling, P. Hall, and M. J. Phillips, Bootstrap Confidence Regions for the Intensity of a Poisson Point Process, Journal of the American Statistical Association, vol.45, issue.436, p.91, 1996.
DOI : 10.2307/1403360

D. D. Cox and F. O. Sullivan, Asymptotic Analysis of Penalized Likelihood and Related Estimators, The Annals of Statistics, vol.18, issue.4, pp.1676-1695, 1990.
DOI : 10.1214/aos/1176347872

D. D. Cox and F. O. Sullivan, Penalized likelihood-type estimators for genaralized nonparametric regression, Journal of multivariate analysis, vol.516, pp.185-206, 1996.

D. Dacunha-castelle and D. Florens-zmirou, Estimation of the coefficients of a diffusion from discrete observations, Stochastics, vol.19, issue.4, pp.263-284, 1986.
DOI : 10.1070/RM1975v030n01ABEH001400

D. Dacunha-castelle and E. Gassiat, Testing the order of a model using locally parameterization : population mixtures and stationary arma processes, Ann. Stat, vol.4, 1997.

R. Davis and W. Mccormick, Estimation for first-order autoregressive processes with positive or bounded innovations, Stochastic Processes and their Applications, pp.237-250, 1989.
DOI : 10.1016/0304-4149(89)90090-2

A. C. Davison, Statistical models. Cambridge series in probability and statistics, 2003.
URL : https://hal.archives-ouvertes.fr/hal-00790104

A. C. Davison and N. L. Ramesh, Local likelihood smoothing of sample extremes, Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.62, issue.1, 1999.
DOI : 10.1111/1467-9868.00228

D. B. Percival and D. A. Rothrock, ???Eyeballing??? Trends in Climate Time Series: A Cautionary Note, Journal of Climate, vol.18, issue.6, pp.886-891, 2005.
DOI : 10.1175/JCLI-3300.1

L. De-hann, On regular variation and its application to the weak convergence of sample extremes. Thesis, 1970.

L. De-hann and A. Ferreira, Extreme value theory : An introduction. Springer series in operations research and financial engineering, 2006.
DOI : 10.1007/0-387-34471-3

S. Dessai and M. Hulme, Does climate policy need probabilities ? Tyndall Centre Working Paper No, p.43, 2003.
DOI : 10.1080/14693062.2004.9685515

P. Doukhan, Mixing : properties and examples, 1994.

F. E. Benth and J. Saltyte-benth, Stochastic Modelling of Temperature Variations with a View Towards Weather Derivatives, Applied Mathematical Finance, vol.28, issue.1, 2004.
DOI : 10.1007/978-1-4612-0949-2

D. R. Easterling, G. A. Meehl, C. Parmesan, S. A. Changnon, T. R. Karl et al., Climate Extremes: Observations, Modeling, and Impacts, Science, vol.289, issue.5487, pp.2068-2074, 2000.
DOI : 10.1126/science.289.5487.2068
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.567.1038

P. Embrechts, C. Klppelberg, and T. Mikosch, Modelling of extremal events in insurance and finance, ZOR Zeitschrift f???r Operations Research Mathematical Methods of Operations Research, vol.73, issue.1, 1997.
DOI : 10.1007/BF01440733

K. Tank and A. M. , Daily dataset of 20th-century surface air temperature and precipitation series for the European Climate Assessment, International Journal of Climatology, vol.14, issue.1, pp.1441-1453, 2002.
DOI : 10.1002/joc.773

F. Comte and Y. Rozenholc, A new algorithm for fixed design regression and denoising, Annals of the Institute of Statistical Mathematics, vol.63, issue.2, pp.449-473, 2004.
DOI : 10.1007/BF02530536
URL : https://hal.archives-ouvertes.fr/hal-00748959

F. Dornier and M. Querel, Pricing weather derivatives by marginal value, Quantitative Finance, vol.1, 2000.

J. Fan, Design-adaptive Nonparametric Regression, Journal of the American Statistical Association, vol.26, issue.420, pp.998-1004, 1992.
DOI : 10.1214/aos/1176343886

J. Fan, Local Linear Regression Smoothers and Their Minimax Efficiencies, The Annals of Statistics, vol.21, issue.1, pp.196-216, 1993.
DOI : 10.1214/aos/1176349022

J. Fan and I. Gijbels, Local polynomial modelling and its application, 1996.
DOI : 10.1007/978-1-4899-3150-4

J. Fan and Q. Yao, Efficient estimation of conditional variance functions in stochastic regression, Biometrika, vol.85, issue.3, pp.645-660, 1998.
DOI : 10.1093/biomet/85.3.645

J. Faraway, Bootstrap selection of bandwidth and confidence bands for nonparametric regression, Journal of Statistical Computation and Simulation, vol.50, issue.1-2, p.3744, 1990.
DOI : 10.2307/2288570

C. A. Ferro, A. Hannachi, and D. Stephenson, Simple non-parametric techniques for exploring changing probability distributions of weather

D. Florens-zmirou, Estimation de la variance d'une diffusion partir d'une observation discrétisée, pp.195-200, 1989.

R. Foutz and R. C. Srivastava, Statistical inference for Markov processes when the model is incorrect, Advances in Applied Probability, vol.11, issue.04, pp.737-749, 1979.
DOI : 10.1214/aoms/1177729952

M. Francisco-fernández and J. M. Vilar-fernández, LOCAL POLYNOMIAL REGRESSION ESTIMATION WITH CORRELATED ERRORS, Communications in Statistics - Theory and Methods, vol.83, issue.7, pp.1271-1293, 2001.
DOI : 10.1137/1104015

M. Francisco-fernández and J. M. Vilar-fernández, Bandwidth selection for the local polynomial estimator under dependence: A simulation study, Computational Statistics, vol.68, issue.4, pp.539-558, 2005.
DOI : 10.1007/BF02741314

P. H. Franses and R. Paap, Periodic Time Series Models, 2004.
DOI : 10.1093/019924202X.001.0001

P. Frich, Observed coherent changes in climatic extremes during the second half of the twentieth century, Climate Research, vol.19, pp.193-212, 2002.
DOI : 10.3354/cr019193

E. Gobet, M. Hoffmann, and M. Reib, Nonparametric estimation of scalar diffusions based on low frequency data, Ann. Stat, vol.32, pp.2223-2253, 2004.

J. Granger and C. W. , Seasonality : Causation, interpretation and implications . Seasonal Analysis of econometric time series, 1978.
DOI : 10.1017/cbo9780511753961.005

P. J. Green and B. W. Silverman, Nonparametric regression and generalized linear models : A roughness penalty approach, 1994.
DOI : 10.1007/978-1-4899-4473-3

C. Gu, Cross-Validating Non-Gaussian Data, Journal of Computational and Graphical Statistics, vol.1, issue.2, pp.169-179, 1992.
DOI : 10.2307/1390840

C. Gu, Smoothing spline ANOVA models, 2002.

X. Guyon and S. Souchet, Estimation de Yule-Walker d'un CAR(p) observ temps discret, Ann. IHP, Proba. et Stat, vol.38, 2002.

H. Wang, Nonlinear ARMA models with functional MA coefficients, Journal of Time Series Analysis, vol.43, issue.6, pp.1032-1056, 2008.
DOI : 10.1111/j.1467-9892.2008.00594.x

P. Hall, Edgeworth expansions for nonparametric density estimators, with applications, Statistics, vol.88, issue.2, p.215232, 1992.
DOI : 10.1214/aos/1176350933

P. Hall, On Bootstrap Confidence Intervals in Nonparametric Regression, The Annals of Statistics, vol.20, issue.2, pp.695-711, 1992.
DOI : 10.1214/aos/1176348652

P. Hall and R. J. , Variance function estimation in regression : the effect of estimating the mean, J.Roy.Statist.Soc.Ser. B, vol.51, pp.3-14, 1989.

P. Hall, S. N. Lahiri, and J. Polzehl, On bandwidth choice in nonparametric regression with both short- and long-range dependent errors, The Annals of Statistics, vol.23, issue.6
DOI : 10.1214/aos/1034713640

P. Hall and N. Tajvidi, Nonparametric analysis of temporal trend when fitting parametric models to extreme value data, Statistical Science, vol.15, pp.153-167, 2000.

L. P. Hansen, J. A. Scheinkman, and N. Touzi, Spectral methods for identifying scalar diffusions, Journal of Econometrics, vol.86, issue.1, pp.1-32, 1998.
DOI : 10.1016/S0304-4076(97)00107-3

E. Härdle and W. Mammen, Comparing Nonparametric Versus Parametric Regression Fits, The Annals of Statistics, vol.21, issue.4, 1993.
DOI : 10.1214/aos/1176349403

W. Härdle, P. Hall, and J. S. Marron, How far are automatically chosen regression smoothing parameters from their optimum, Journal of The American Statistical Association, vol.83, pp.86-95, 1988.

J. D. Hart, Automated kernel smoothing of dependent data by using time series cross-validation, Journal of the Royal Statistical Association , Series B, vol.56, pp.529-542, 1994.

T. Hastie and C. Loader, Local Regression: Automatic Kernel Carpentry, Statistical Science, vol.8, issue.2, pp.120-143, 1993.
DOI : 10.1214/ss/1177011002
URL : http://projecteuclid.org/download/pdf_1/euclid.ss/1177011002

T. J. Hastie and R. J. Tibshirani, Generalized Additive Models, 1990.

M. R. Haylock, N. Hofstra, A. M. Klein-tank, E. J. Klok, P. D. Jones et al., A European daily high-resolution gridded data set of surface temperature and precipitation for 1950???2006, Journal of Geophysical Research, vol.32, issue.D20, 2008.
DOI : 10.1029/2008JD010201

T. T. Hoang, Séries chronologiques non stationnaires non linéaires. Le cas des séries de températures en Europe, 2009.

T. T. Hoang, D. Dacunha-castelle, and S. Parey, Testing non stationnarity . Application to trends and extremes for temperature in climate models, 2009.

T. T. Hoang, S. Parey, and D. Dacunha-castelle, Multidimensional trends: The example of temperature, The European Physical Journal Special Topics, vol.174, issue.1, pp.113-124, 2009.
DOI : 10.1140/epjst/e2009-01094-6

S. Hylleberg, Seasonality in Regression, 1986.

S. H. Inge, Minimal conditions for weak convergence to a diffusion process on the line, Ann. Probab, vol.9, 1981.

J. D. Hart, Kernel regression estimation with time series errors, Journal of the Royal Statistical Society (B), vol.53, pp.173-187, 1991.

S. Jewson and R. Caballero, The use of weather forecasts in the pricing of weather derivatives, Meteorological Applications, vol.81, issue.4, pp.377-389, 2003.
DOI : 10.1017/S1350482703001099

J. K. Angell, Effect of Exclusion of Anomalous Tropical Stations on Temperature Trends from a 63-Station Radiosonde Network, and Comparison with Other Analyses, Journal of Climate, vol.16, issue.13, pp.2288-2295, 2003.
DOI : 10.1175/2763.1

R. Katz and B. Brown, Extreme events in a changing climate: Variability is more important than averages, Climatic Change, vol.71, issue.3, pp.289-302, 1992.
DOI : 10.1007/BF00139728

R. W. Katz, M. B. Parlange, and P. Naveau, Statistics of extremes in hydrology, Advances in Water Resources, vol.25, issue.8-12, p.12871304, 2002.
DOI : 10.1016/S0309-1708(02)00056-8

M. Kessler, Estimation of an Ergodic Diffusion from Discrete Observations, Scandinavian Journal of Statistics, vol.24, issue.2, pp.211-229, 1997.
DOI : 10.1111/1467-9469.00059

M. Kessler and M. Sorensen, Estimating Equations Based on Eigenfunctions for a Discretely Observed Diffusion Process, Bernoulli, vol.5, issue.2, pp.299-314, 1999.
DOI : 10.2307/3318437

D. Kiktev, D. M. Sexton, L. Alexander, and F. C. , Comparison of Modeled and Observed Trends in Indices of Daily Climate Extremes, Journal of Climate, vol.16, issue.22, pp.3560-3571, 2003.
DOI : 10.1175/1520-0442(2003)016<3560:COMAOT>2.0.CO;2

A. M. Klein-tank, G. P. Können, and F. M. Selten, Signals of anthropogenic influence on European warming as seen in the trend patterns of daily temperature variance, International Journal of Climatology, vol.16, issue.1, pp.1-16, 2005.
DOI : 10.1002/joc.1087

S. N. Lahiri, Resampling methods for dependent data, 2003.
DOI : 10.1007/978-1-4757-3803-2

M. R. Leadbetter, G. Lindgren, and H. Rootzén, Extremes and related properties of random sequences and series, 1983.

C. Loader, Local Regression and Likelihood, 1999.

J. Lu, G. A. Vecchi, and T. Reichler, Expansion of the hadley cell under global warming, Geophysical Research Letter, vol.34, 2007.

M. Moreno, Evaluation des dérivés climatiques. Speedwell Weather Derivatives Limited, 2000.

M. Mudelsee, M. Brngen, M. Deutsch, and G. Tetzlaff, Trends in flood risk of the River Werra (Germany) over the past 500 years, Hydr. Sciences J, vol.51, p.833, 2006.

J. S. Marron, Partitioned cross-validation, Econometric Reviews, vol.9, issue.2, pp.271-284, 1987.
DOI : 10.1214/aos/1176346792

E. Masry and J. Fan, Local Polynomial Estimation of Regression Functions for Mixing Processes, Scandinavian Journal of Statistics, vol.24, issue.2, pp.165-179, 1997.
DOI : 10.1111/1467-9469.00056

M. Mudelsee, Break function regression, The European Physical Journal Special Topics, vol.174, issue.1, pp.49-63, 2009.
DOI : 10.1140/epjst/e2009-01089-3

H. G. Müller and U. Stadtmüller, Estimation of Heteroscedasticity in Regression Analysis, The Annals of Statistics, vol.15, issue.2, pp.610-625, 1987.
DOI : 10.1214/aos/1176350364

M. Nerlove, Spectral Analysis of Seasonal Adjustment Procedures, Econometrica, vol.32, issue.3, pp.241-286, 1964.
DOI : 10.2307/1913037

M. H. Neumann, Automatic bandwidth choice and confidence intervals in nonparametric regression, The Annals of Statistics, vol.23, issue.6, pp.1937-1959, 1995.
DOI : 10.1214/aos/1034713641

M. H. Neumann, Pointwise Confidence Intervals in Nonparametric Regression with Heteroscedastic Error Structure, Statistics, vol.8, issue.1, pp.1-36, 1997.
DOI : 10.1214/aos/1176350142

M. Nogaj, S. Parey, and D. Dacunha-castelle, Non-stationary extreme models and a climatic application, Nonlinear Processes in Geophysics, vol.14, issue.3, pp.305-316, 2007.
DOI : 10.5194/npg-14-305-2007
URL : https://hal.archives-ouvertes.fr/hal-00331100

N. S. Altman, Kernel Smoothing of Data with Correlated Errors, Journal of the American Statistical Association, vol.253, issue.411, pp.749-759, 1990.
DOI : 10.1214/aos/1176346710

J. D. Opsomer, Nonparametric regression in the presence of correlated errors. Modelling Longitudinal and Spatially correlated data : Methods, Application and Future directions, pp.339-348, 1997.

J. Opsomer, Y. Wang, and Y. Yang, Nonparametric regression with correlated errors, Statistical Science, vol.16, pp.134-153, 2001.

F. Osullivan, B. S. Yandell, and W. J. Raynor, Automatic Smoothing of Regression Functions in Generalized Linear Models, Journal of the American Statistical Association, vol.4, issue.393, pp.96-103, 1986.
DOI : 10.1080/01621459.1986.10478243

P. Yiou, D. Dacunha-castelle, S. Parey, and T. T. Hoang, Statistical representation of temperature mean and variability in Europe, Geophysical Research Letters, vol.31, issue.4, 2009.
DOI : 10.1029/2008GL036836

S. Parey, Extremely high temperatures in France at the end of the century, Climate Dynamics, vol.52, issue.3, pp.99-112, 2008.
DOI : 10.1007/s00382-007-0275-4

S. Parey, D. Dacunha-castelle, and T. T. Hoang, Mean and variance evolutions of the hot and cold temperatures in Europe, Climate Dynamics, vol.427, issue.21, 2009.
DOI : 10.1007/s00382-009-0557-0

S. Parey, T. T. Hoang, and D. Dacunha-castelle, Different ways to compute temperature return levels in the climate change context, Environmetrics, vol.17, issue.4, 2009.
DOI : 10.1002/env.1060

S. Parey, F. Malek, C. Laurent, and D. Dacunha-castelle, Trends and climate evolution: Statistical approach for very high temperatures in France, Climatic Change, vol.50, issue.3-4, pp.331-352, 2007.
DOI : 10.1007/s10584-006-9116-4

F. Pauli and S. G. Coles, Penalized likelihood inference in extreme value analyses, Journal of Applied Statistics, vol.40, issue.5, pp.547-560, 2001.
DOI : 10.1016/0022-1694(88)90037-6

R. Perfeckt, Extremal behaviour of stationart markov chains and application . The Annals of Applied Probability, pp.529-548, 1994.

S. Pezzulli, D. B. Stephenson, and A. Hannachi, The Variability of Seasonality, Journal of Climate, vol.18, issue.1, pp.71-88, 2005.
DOI : 10.1175/JCLI-3256.1

J. Pickands, Statistical inference using extreme order statistics, Ann. Stat, vol.3, pp.119-131, 1975.

V. Piterbarg, Discrete and Continuous Time Extremes of Gaussian Processes, Extremes, vol.2, issue.2, pp.161-177, 2004.
DOI : 10.1007/s10687-005-6198-8

B. L. Prakasa-rao, Statistical inference from sampled data for stochastic processes, Contemp. Math, vol.80, pp.249-284, 1988.
DOI : 10.1090/conm/080/999016

P. Prescott and A. T. Walden, Maximum likelihood estimation of the parameters of the generalized extreme-value distribution, Biometrika, vol.67, issue.3, pp.723-724, 1980.
DOI : 10.1093/biomet/67.3.723

R. A. Davis, Maximum and minimum of one-dimensional diffusions, Stochastic Processes and their applications, pp.1-9, 1982.
DOI : 10.1016/0304-4149(82)90002-3

N. I. Ramesh and A. C. Davison, Local models for exploratory analysis of hydrological extremes, Journal of Hydrology, vol.256, issue.1-2, pp.106-119, 2002.
DOI : 10.1016/S0022-1694(01)00522-4

C. Reinsch, Smoothing by spline functions, Numerische Mathematik, vol.10, issue.3, pp.177-183, 1967.
DOI : 10.1007/BF02162161

J. Rice and M. Rosenblatt, Smoothing Splines: Regression, Derivatives and Deconvolution, The Annals of Statistics, vol.11, issue.1, pp.141-156, 1983.
DOI : 10.1214/aos/1176346065

C. Y. Robert, J. Segers, and C. A. Ferro, A sliding blocks estimator for the extremal index, Electronic Journal of Statistics, vol.3, issue.0, pp.993-1020, 2009.
DOI : 10.1214/08-EJS345

H. Rootzén, Weak convergence of the tail empirical process for dependent sequences, Stochastic Processes and their Applications, vol.119, issue.2, pp.468-490, 2009.
DOI : 10.1016/j.spa.2008.03.003

G. Rosen and A. Cohen, Extreme percentile regression. Statistical Theory and Computational Aspects of Smoothing : Proceedings of the COMPSTAT '94 satelilite meeting, pp.200-214, 1994.
DOI : 10.1007/978-3-642-48425-4_15

O. Roustand, L. J. , X. Bay, and L. Carraro, Model risk in the pricing of weather derivatives, Ecole des Mines, 2003.

D. Ruppert and M. P. Wand, Multivariate Locally Weighted Least Squares Regression, The Annals of Statistics, vol.22, issue.3, pp.1346-1370, 1994.
DOI : 10.1214/aos/1176325632

D. Ruppert, M. P. Wand, U. Holst, and O. Hössjer, Local Polynomial Variance-Function Estimation, Technometrics, vol.5, issue.3, pp.262-273, 1997.
DOI : 10.1080/00401706.1997.10485117
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.54.9389

H. Schäbe, Nonparametric estimation of intensities of nonhomogeneous Poisson processes, Statistical Papers, vol.82, issue.1, pp.113-131, 1993.
DOI : 10.1007/BF02925534

C. Schär, P. L. Vidale, D. Lüthi, C. Frei, C. Hberli et al., The role of increasing temperature variability in European summer heatwaves, Nature, vol.427, issue.6972, pp.332-336, 2004.
DOI : 10.1038/nature02300

I. J. Schoenberg, Spline functions and the problem of graduation, Proceedings of the National academy of sciences of the United States, pp.947-950, 1964.

C. D. Schönwise, F. Rapp, J. , and M. Denhard, Observed climate trends in europe 1891-1990, Meteorologische Zeitschrift, vol.3, pp.22-28, 1994.

C. Schwierz, C. Appenzeller, H. C. Davies, M. A. Liniger, W. Müller et al., Challenges posed by and approaches to the study of seasonal-to-decadal climate variability Climate change, 2006.

S. I. Resnick, Extreme values, regular variation and point processes, 1987.
DOI : 10.1007/978-0-387-75953-1

S. I. Resnick, Heavy-Tail Phenomena : Probabilistic and Statistical Modeling, 2007.

J. S. Simonoff, Smoothing methods in Statistics, 1996.
DOI : 10.1007/978-1-4612-4026-6

R. L. Smith, Maximum likelihood estimation in a class of nonregular cases, Biometrika, vol.72, issue.1, pp.67-90, 1985.
DOI : 10.1093/biomet/72.1.67

R. L. Smith, Extreme Value Analysis of Environmental Time Series: An Application to Trend Detection in Ground-Level Ozone, Statistical Science, vol.4, issue.4, pp.367-393, 1989.
DOI : 10.1214/ss/1177012400

R. L. Smith, S. Tawn, and . Coles, Markov chain models for threshold exceedances, Biometrika, vol.84, issue.2, pp.249-268, 1997.
DOI : 10.1093/biomet/84.2.249
URL : http://biomet.oxfordjournals.org/cgi/content/short/84/2/249

Q. Song and L. Yang, Spline confidence bands for variance functions, Journal of Nonparametric Statistics, vol.6, issue.5, pp.589-609, 2009.
DOI : 10.1017/CBO9780511755453
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.157.9482

S. Souchet, Estimation des paramtres dune diffusion ergodique observe temps discret, 1998.

C. J. Stone, Consistent Nonparametric Regression, The Annals of Statistics, vol.5, issue.4, pp.595-620, 1977.
DOI : 10.1214/aos/1176343886
URL : http://projecteuclid.org/download/pdf_1/euclid.aos/1176343886

G. Wahba, Smoothing noisy data with spline functions, Numerische Mathematik, vol.25, issue.2, pp.383-393, 1975.
DOI : 10.1007/BF01437407

J. Wang and L. Yang, Polynomial spline confidence bands for regression curves, Statist. Sinica, vol.19, pp.325-343, 2009.

Y. Wang, Smoothing Spline Models with Correlated Random Errors, Journal of the American Statistical Association, vol.23, issue.441, pp.341-348, 1998.
DOI : 10.1080/01621459.1986.10478377

E. Whittaker, On a new method of graduation, Proceedings of the Edinburgh Mathematical society, pp.63-75, 1923.

Z. Wu and N. E. Huang, ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD, Advances in Adaptive Data Analysis, vol.01, issue.01, 2008.
DOI : 10.1142/S1793536909000047

Z. Wu, N. E. Huang, S. R. Long, and C. K. Peng, On the trend, detrending, and variability of nonlinear and nonstationary time series, Proceedings of the National Academy of Sciences, vol.104, issue.38, 2007.
DOI : 10.1073/pnas.0701020104

Z. Wu, The modulated annual cycle: an alternative reference frame for climate anomalies, Climate Dynamics, vol.115, issue.11, pp.823-841, 2008.
DOI : 10.1007/s00382-008-0437-z

Y. Aitsahalia, Maximum Likelihood Estimation of Discretely Sampled Diffusions: A Closed-form Approximation Approach, Econometrica, vol.70, issue.1, pp.223-262, 2002.
DOI : 10.1111/1468-0262.00274

Z. Yan, Trends of Extreme Temperatures in Europe and China Based on Daily Observations, Climatic change, vol.53, pp.355-392, 2002.
DOI : 10.1007/978-94-010-0371-1_13

N. Yoshida, Estimation for diffusion processes from discrete observation, Journal of Multivariate Analysis, vol.41, issue.2, pp.220-242, 1992.
DOI : 10.1016/0047-259X(92)90068-Q