The magnitude of the trends of environmental and climatic changes is mostly derived from the slopes of the linear trends using ordinary least-square fitting. An alternative flexible fitting model, piecewise regression, has been applied here to surface air temperature records in southeastern Spain for the recent warming period (1973–2014) to gain accuracy in the description of the inner structure of change, dividing the time series into linear segments with different slopes. Breakpoint years, with confidence intervals (CIs), were estimated and separated periods of significant trend change were determined. First, simple linear trends for mean, maximum and minimum surface air temperatures and diurnal temperature range (DTR) from the four longest and most reliable historic records in SE Spain were estimated. All series in the region showed intense linear warming signs during the period 1973–2014. However, updated warming trends were lower than those previously cited for the region and Spain from the 1970s onwards. Piecewise regression model allowed us to detect breakpoints in the series, and the absence of significant trends in the most recent period of the segmented fits for two stations. In general, piecewise regression model showed better fit than simple linear regression model, and thus, showed a better description of temperature variability.