  In recent years, SPDEs have become a well-studied field in mathematics. With
their increase in popularity, it becomes important to efficiently approximate
their solutions. Thus, our goal is a contribution towards the development of
efficient and practical time-stepping methods for SPDEs.
  Operator splitting schemes are a powerful tool for deterministic and
stochastic differential equations. An example is given by domain decomposition
schemes, where we split the domain into sub-domains. Instead of solving one
expensive problem on the entire domain, we deal with cheaper problems on the
sub-domains. This is particularly useful in modern computer architectures, as
the sub-problems may often be solved in parallel. While splitting methods have
already been used to study domain decomposition methods for deterministic PDEs,
this is a new approach for SPDEs.
  We provide an abstract convergence analysis of a splitting scheme for
stochastic evolution equations and state a domain decomposition scheme as an
application of the setting. The theoretical results are verified through
numerical experiments.
