  With climate extremes' rising frequency and intensity, robust analytical
tools are crucial to predict their impacts on terrestrial ecosystems. Machine
learning techniques show promise but require well-structured, high-quality, and
curated analysis-ready datasets. Earth observation datasets comprehensively
monitor ecosystem dynamics and responses to climatic extremes, yet the data
complexity can challenge the effectiveness of machine learning models. Despite
recent progress in deep learning to ecosystem monitoring, there is a need for
datasets specifically designed to analyse compound heatwave and drought extreme
impact. Here, we introduce the DeepExtremeCubes database, tailored to map
around these extremes, focusing on persistent natural vegetation. It comprises
over 40,000 spatially sampled small data cubes (i.e. minicubes) globally, with
a spatial coverage of 2.5 by 2.5 km. Each minicube includes (i) Sentinel-2 L2A
images, (ii) ERA5-Land variables and generated extreme event cube covering 2016
to 2022, and (iii) ancillary land cover and topography maps. The paper aims to
(1) streamline data accessibility, structuring, pre-processing, and enhance
scientific reproducibility, and (2) facilitate biosphere dynamics forecasting
in response to compound extremes.
