Abstract. Quality control and homogenization has to be undertaken prior to any data analysis in order to eliminate any erroneous values and non climatic biases in time series. In this work we describe and then apply our own approach to data quality control, combining several methods: (i) by applying limits derived from interquartile ranges (ii) by analyzing difference series between candidate and neighbouring stations and (iii) by comparing the series values tested with "expected'' values – technical series created by means of statistical methods for spatial data (e.g. IDW, kriging).

Because of the presence of noise in series, statistical homogeneity tests render results with some degree of uncertainty. In this work, the use of various statistical tests and reference series made it possible to increase considerably the number of homogeneity test results for each series and thus to assess homogeneity more reliably. Inhomogeneities were corrected on a daily scale.

These methodological approaches are demonstrated by use of the daily data of air temperature and precipitation measured in the area of the Czech Republic. Series were processed by means of developed ProClimDB and AnClim software (http://www.climahom.eu).