Presentation delivered at EGU 2022 (Vienna, Austria): End users of climate change information are relying on climate services and tools in order to produce meaningful information for specific applications. Data volumes as well as the number of datasets are increasing very rapidly, and the ability to select, process and download all needed data is getting complex, technical and very time-consuming. It is especially true since those datasets are often distributed among several data centres and into a large quantity of files. Several platform are being developed to hide this complexity to users and provide a seamless access to climate data, as well as to provide on-demand data analysis capabilities. We can cite, for example, the Copernicus Data Store (CDS https://cds.climate.copernicus.eu), along with its toolbox to perform online data analysis. Another platform is developed within the H2020 IS-ENES3 project, called climate4impact (C4I 2.0 https://dev.climate4impact.eu ). It is using an enhanced Jupyter-Lab environment called SWIRRL (Software for Interactive Reproducible Research Labs https://gitlab.com/KNMI-OSS/swirrl ) along with a collection of Jupyter notebooks (https://gitlab.com/is-enes-cdi-c4i/notebooks) as useful set of example on how to use the data. Finally, the portal provides interactive pages for the evaluation of climate models (using ESMValTool) to guide users on selecting climate datasets. The notebooks that can be executed in C4I, are developed using a very convenient software library, which is made available via SWIRRL, to calculate climate indices and indicators called icclim (v5.0 https://github.com/cerfacs-globc/icclim ). This library, which is also in the process of being integrated into the C3S, is a flexible python software package to calculate climate indices and indicators. This tool adhere as much as possible to metadata conventions such as the Climate & Forecasting Conventions (CF-1.x) as well as the clix-meta (https://github.com/clix-meta) work that is being done in IS-ENES3. Proper provenance information still needs to be added. The ultimate goal is to be as close as possible to all FAIR aspects. icclim is designed with performance and optimisation in mind, because the goal is to provide on-demand calculations for users. It provides the implementation of most of the international standard climate indices such as ECAD, ETCCDI, ET-SCI, including the correct methodology for calculating percentile indices using the bootstrapping method. It has been validated against R.Climdex as well (https://cran.r-project.org/web/packages/climdex.pcic/index.html). This new 5.x version of icclim is based on functions from the xclim (https://github.com/Ouranosinc/xclim) python library, which was inspired by earlier versions of icclim, but using xarray and dask for data access and processing. In this presentation, the climate4impact 2.0 platform will be described along with the icclim climate indices tool. Important metadata aspects will also be discussed (clix-meta). A few examples using the jupyter notebook collection will be shown.