The oncoming climate changes are the biggest challenge the mankind is faced with. The impacts of climate change are manifold and vary regionally, even locally, in their severity. For decades, most analyses of long-term global climate change using observational temperature and precipitation data have focused on changes in mean values. However, immediate damages to humans and their properties as well as the ecosystems, are not obviously caused by gradual changes in these variables but mainly by so-called extreme climate events. The relative rare occurrence of extremes makes it necessary to investigate long data records to determine significant changes in the frequency and intensity of extreme events. There are various methods to investigate extreme events, but the computation and analysis of climate indices (Cis) derived from daily data is probably the most widely used non-parametric approach. In order to detect changes in climate extremes, it is important to develop a set of indices that are statistically robust, cover a wide range of climates, and have a high signal-to-noise ratio. The Cis are numerical indicators, which are carefully designed to encompass magnitude (e.g., hot-day threshold), frequency (e.g., heavy rainfall days) and persistence (e.g., longest dry period) of climate extremes. They include absolute-thresholds indices, percentile-based indices, and indices based on the duration of an event. They are used in several projects on climate change with focus on at different spatial scales, from planetary to continental, regional, national or local scale, as prevailing indicators of changes of the extreme events. As far as many of these studies uses partially pre-existing datasets of CIs, the availability of such databases could facilitate any future work, which relies more or less on Cis-based analysis of the present climate. The objective of the present project is to construct and present to the expert community for barrier-free use a comprehensive suite of climate indices datasets (called ClimData), computed from reliable and up-to-date input data from one side and well elaborated and internationally accepted methodology from other. Hence the importance of assessing trends in climate extremes is often emphasized (see literature), estimations of the magnitude of the trend as well as its statistical significance, are accepted as 'natural' supplement to the Cis-time series. Thus, such information for all indices on seasonal and annual basis, is included in ClimData also.