The simultaneous variability of several climate extremes in Romania on the one hand and the understanding of the large-scale mechanisms responsible for this variability on the other are examined. Ten indices associated with temperature and precipitation extremes computed at high spatial resolution for the period 1961–2010 are analysed. Significant increasing trends for the temperature extremes are detected in all seasons, except for autumn, with the highest increasing rate in summer and the lowest in spring. Regarding precipitation extremes, significant increasing trends over large areas in the frequency of very wet days and maximum daily amount during autumn and in the maximum duration of dry spells during summer are the only ones detected.
The large-scale mechanisms responsible for these characteristics of variability, especially the simultaneous variability of several climate extremes, are identified through the canonical correlation analysis applied to a combination of various large-scale predictors and to combined climate extremes.
In winter, it was found that the thermodynamic factor (represented by air temperature anomalies at 850 hPa) mainly controls the trends of temperature extremes in Romania, whereas the dynamic one (represented by the sea level pressure anomalies) controls the pattern of trend magnitude. Regarding precipitation extremes, the role of the two factors is reversed. The Carpathians' influence is noted for this season. In summertime, the thermodynamic factor is dominant for both temperature and precipitation extremes analysed in this article. For temperature extremes, the T850 alone could explain their variability characteristics, whereas for precipitation extremes (frequency and duration) the SH700 has the dominant role, except for the maximum duration of dry intervals, which is controlled by a combination of T850 and SH700 anomalies. The connections found in this study are strong and explain a great part of the total observed variance, showing that these results can be used in a future study to build skilful statistical downscaling models, simultaneously for several seasonal climate extremes, giving the results more physical coherence.