International audienceThe interdependence between climatic variables should be taken into account when developing climate scenarios. For example, temperature-precipitation interdependence in the Arctic is strong and impacts on other physical characteristics, such as the extent and duration of snow cover. However, this interdependence is often misrepresented in climate simulations. Here we use two two-dimensional (2-D) methods for statistically adjusting climate model simulations to develop plausible local daily temperature (Tmean) and precipitation (Pr) scenarios. The first 2-D method is based on empirical quantile mapping (2Dqm) and the second on parametric copula models (2Dcopula). Both methods are improved here by forcing the preservation of the modeled long-term warming trend and by using moving windows to obtain an adjustment specific to each day of the year. These methods were applied to a representative ensemble of 13 global climate model simulations at 26 Canadian Arctic coastal sites and tested using an innovative cross-validation approach. Intervariable dependence was evaluated using correlation coefficients and empirical copula density plots. Results show that these 2-D methods, especially 2Dqm, adjust individual distributions of climatic time series as adequately as one common one-dimensional method (1Dqm) does. Furthermore, although 2Dqm outperforms the other methods in reproducing the observed temperature-precipitation interdependence over the calibration period, both 2Dqm and 2Dcopula perform similarly over the validation periods. For cases where temperature-precipitation interdependence is important (e.g., characterizing extreme events and the extent and duration of snow cover), both 2-D methods are good options for producing plausible local climate scenarios in Canadian Arctic coastal zones

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