[1] This paper presents an intercomparison of eight statistical tests to detect inhomogeneities in climatic data. The objective was to select those that are more suitable for precipitation data in the southern and central regions of the province of Quebec, Canada. The performances of these methods were evaluated by simulation on several thousands of homogeneous and inhomogeneous synthetic series. These series were generated to reproduce the statistical characteristics of typical precipitations observed in the southern and central parts of the province of Quebec and nearby areas, Canada. It was found that none of these methods was efficient for all types of inhomogeneities, but some of them performed substantially better than others: the bivariate test, the Jaruskova's method, and the standard normal homogeneity test. Techniques such as the Student sequential test and the two-phase regression led to the worst performances. The analysis of the performances of each method in several situations allowed the design of an optimal procedure that takes advantage of the strengths of the best performing techniques.

Triggering of alert days and ultimately the initiation of emergency responses by a heat-health warning system varies significantly across approaches adopted to establish triggers.

Trends and variations in daily temperature and precipitation indices in southern Québec