In the last few decades, the size of spatial and spatio-temporal datasets in
many research areas has rapidly increased with the development of data
collection technologies. As a result, classical statistical methods in spatial
statistics are facing computational challenges. For example, the kriging
predictor in geostatistics becomes prohibitive on traditional hardware
architectures for large datasets as it requires high computing power and memory
footprint when dealing with large dense matrix operations. Over the years,
various approximation methods have been proposed to address such computational
issues, however, the community lacks a holistic process to assess their
approximation efficiency. To provide a fair assessment, in 2021, we organized
the first competition on spatial statistics for large datasets, generated by
our {\em ExaGeoStat} software, and asked participants to report the results of
estimation and prediction. Thanks to its widely acknowledged success and at the
request of many participants, we organized the second competition in 2022
focusing on predictions for more complex spatial and spatio-temporal processes,
including univariate nonstationary spatial processes, univariate stationary
space-time processes, and bivariate stationary spatial processes. In this
paper, we describe in detail the data generation procedure and make the
valuable datasets publicly available for a wider adoption. Then, we review the
submitted methods from fourteen teams worldwide, analyze the competition
outcomes, and assess the performance of each team