diff --git a/Project.toml b/Project.toml
index cac573a7d8..1d62cb9034 100644
--- a/Project.toml
+++ b/Project.toml
@@ -66,7 +66,7 @@ DiffEqCallbacks = "2.25"
 EllipsisNotation = "1.0"
 FillArrays = "0.13.2, 1"
 ForwardDiff = "0.10.18"
-HDF5 = "0.14, 0.15, 0.16"
+HDF5 = "0.14, 0.15, 0.16, 0.17"
 IfElse = "0.1"
 LinearMaps = "2.7, 3.0"
 LoopVectorization = "0.12.118"
diff --git a/docs/src/parallelization.md b/docs/src/parallelization.md
index 245fdc1185..d56777c9af 100644
--- a/docs/src/parallelization.md
+++ b/docs/src/parallelization.md
@@ -166,17 +166,36 @@ section, specifically at the descriptions of the performance index (PID).
 
 
 ### Using error-based step size control with MPI
-If you use error-based step size control (see also the section on [error-based adaptive step sizes](@ref adaptive_step_sizes))
-together with MPI you need to pass `internalnorm=ode_norm` and you should pass
-`unstable_check=ode_unstable_check` to OrdinaryDiffEq's [`solve`](https://docs.sciml.ai/DiffEqDocs/latest/basics/common_solver_opts/),
+If you use error-based step size control (see also the section on
+[error-based adaptive step sizes](@ref adaptive_step_sizes)) together with MPI you need to pass
+`internalnorm=ode_norm` and you should pass `unstable_check=ode_unstable_check` to
+OrdinaryDiffEq's [`solve`](https://docs.sciml.ai/DiffEqDocs/latest/basics/common_solver_opts/),
 which are both included in [`ode_default_options`](@ref).
 
 ### Using parallel input and output
-Trixi.jl allows parallel I/O using MPI by leveraging parallel HDF5.jl. To enable this, you first need
-to use a system-provided MPI library, see also [here](@ref parallel_system_MPI) and you need to tell
-[HDF5.jl](https://github.com/JuliaIO/HDF5.jl) to use this library.
-To do so, set the environment variable `JULIA_HDF5_PATH` to the local path
-that contains the `libhdf5.so` shared object file and build HDF5.jl by executing `using Pkg; Pkg.build("HDF5")`.
-For more information see also the [documentation of HDF5.jl](https://juliaio.github.io/HDF5.jl/stable/mpi/).
-
-If you do not perform these steps to use parallel HDF5 or if the HDF5 is not MPI-enabled, Trixi.jl will fall back on a less efficient I/O mechanism. In that case, all disk I/O is performed only on rank zero and data is distributed to/gathered from the other ranks using regular MPI communication.
+Trixi.jl allows parallel I/O using MPI by leveraging parallel HDF5.jl. On most systems, this is
+enabled by default. Additionally, you can also use a local installation of the HDF5 library
+(with MPI support). For this, you first need to use a system-provided MPI library, see also
+[here](@ref parallel_system_MPI) and you need to tell [HDF5.jl](https://github.com/JuliaIO/HDF5.jl)
+to use this library. To do so with HDF5.jl v0.17 and newer, set the preferences `libhdf5` and
+`libhdf5_hl` to the local paths of the libraries `libhdf5` and `libhdf5_hl`, which can be done by
+```julia
+julia> using Preferences, UUIDs
+julia> set_preferences!(
+           UUID("f67ccb44-e63f-5c2f-98bd-6dc0ccc4ba2f"), # UUID of HDF5.jl
+           "libhdf5" => "/path/to/your/libhdf5.so",
+           "libhdf5_hl" => "/path/to/your/libhdf5_hl.so", force = true)
+```
+For more information see also the
+[documentation of HDF5.jl](https://juliaio.github.io/HDF5.jl/stable/mpi/). In total, you should
+have a file called LocalPreferences.toml in the project directory that contains a section
+`[MPIPreferences]`, a section `[HDF5]` with entries `libhdf5` and `libhdf5_hl`, a section `[P4est]`
+with the entry `libp4est` as well as a section `[T8code]` with the entries `libt8`, `libp4est`
+and `libsc`.
+If you use HDF5.jl v0.16 or older, instead of setting the preferences for HDF5.jl, you need to set
+the environment variable `JULIA_HDF5_PATH` to the path, where the HDF5 binaries are located and
+then call `]build HDF5` from Julia.
+
+If HDF5 is not MPI-enabled, Trixi.jl will fall back on a less efficient I/O mechanism. In that
+case, all disk I/O is performed only on rank zero and data is distributed to/gathered from the
+other ranks using regular MPI communication.