A baroclinic model for the atmospheric jet at middle-latitudes is used as a stochastic generator of non-stationary time series of the total energy of the system. A linear time trend is imposed on the parameter T E , descriptive of the forced equator-to-pole temperature gradient and responsible for setting the average baroclinicity in the model. The focus lies on establishing a theoretically sound framework for the detection and assessment of trend at extreme values of the generated time series. This problem is dealt with by fitting time-dependent Generalized Extreme Value (GEV) models to sequences of yearly maxima of the total energy. A family of GEV models is used in which the location µ and scale parameters σ depend quadratically and linearly on time, respectively, while the shape parameter ξ is kept constant. From this family, a model is selected by using diagnostic graphical tools, such as probability and quantile plots, and by means of the likelihood ratio test. The inferred location and scale parameters are found to depend in a rather smooth way on time and, therefore, on T E . In particular, power-law dependences of µ and σ on T E are obtained, in analogy with the results of a previous work where the same baroclinic model was run with fixed values of T E spanning the same range as in this case. It is emphasized under which conditions the adopted approach is valid.

A baroclinic model for the atmospheric jet at middle latitudes is used as a stochastic generator of nonstationary time series of the total energy of the system. A linear time trend is imposed on the parameter T E , descriptive of the forced equator-to-pole temperature gradient and responsible for setting the average baroclinicity in the model. The focus lies on establishing a theoretically sound framework for the detection and assessment of trend at extreme values of the generated time series. This problem is dealt with by fitting time-dependent generalized extreme value (GEV) models to sequences of yearly maxima of the total energy. A family of GEV models is used in which the location and scale parameters depend quadratically and linearly on time, respectively, while the shape parameter is kept constant. From this family, a GEV model is selected with Akaike's information criterion, complemented by the likelihood ratio test and by assessment through standard graphical diagnostics. The inferred location and scale parameters are found to depend in a rather smooth way on time and, therefore, on T E . In particular, power-law dependences of and on T E are obtained, in analogy with the results of a previous work where the same baroclinic model was run with fixed values of T E spanning the same range as in this case. It is emphasized under which conditions the adopted approach is valid.

A statistical methodology is proposed and tested for the analysis of extreme values of atmospheric wave activity at mid‐latitudes. The adopted methods are the classical block‐maximum and peak over threshold, respectively based on the generalized extreme value (GEV) distribution and the generalized Pareto distribution (GPD). Time‐series of the ‘Wave Activity Index’ (WAI) and the ‘Baroclinic Activity Index’ (BAI) are computed from simulations of the General Circulation Model ECHAM4.6, which is run under perpetual January conditions. Both the GEV and the GPD analyses indicate that the extremes of WAI and BAI are Weibull distributed, this corresponds to distributions with an upper bound. However, a remarkably large variability is found in the tails of such distributions; distinct simulations carried out under the same experimental setup provide sensibly different estimates of the 200‐yr WAI return level. The consequences of this phenomenon in applications of the methodology to climate change studies are discussed. The atmospheric configurations characteristic of the maxima and minima of WAI and BAI are also examined.

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