  Today's scientific simulations, for example in the high-performance exascale
sector, produce huge amounts of data. Due to limited I/O bandwidth and
available storage space, there is the necessity to reduce scientific data of
high performance computing applications. Error-bounded lossy compression has
been proven to be an effective approach tackling the trade-off between accuracy
and storage space. Within this work, we are exploring and discussing
error-bounded lossy compression solely based on adaptive mesh refinement
techniques. This compression technique is not only easily integrated into
existing adaptive mesh refinement applications but also suits as a general
lossy compression approach for arbitrary data in form of multi-dimensional
arrays, irrespective of the data type. Moreover, these techniques permit the
exclusion of regions of interest and even allows for nested error domains
during the compression. The described data compression technique is presented
exemplary on ERA5 data.
