Recently, i use open_mfdataset to open a local tar.gz file of multiple netcdf files, it failed to open it and raise a distributed.scheduler.KilledWorker: Error and TypeError: cannot serialize 'ExFileObject' object .

My code is like following,

```python import tarfile from dask.distributed import Client

client = Client()

tar = tarfile.open(my_multiple_netcdf_tar_gz_file) flist = [tar.extractfile(member) for member in tar.getmembers()]

ds = xr.open_mfdataset(flist)

This line will raise Exception

print(ds.MyNcVar.values)

....

blah blah my other client calcualation codes

....

client.close() ```

In above code, the elements of variable flist will be type of ExFileObject, which can't be serialized to distributed.Client cluster and therefore will result in the failure of open_mfdataset .

The reason is xr.open_mfdataset auto convert chunks=None to {} , which will force the method xr.open_dataset to use dask.

We can see in this line of open_mfdataset ,

https://github.com/pydata/xarray/blob/37fe5441c8a2fb981f2c50b8379d7d4f8492ae19/xarray/backends/api.py#L897

```python # Notes this line will force chunks=None into chunks={} and result in the involvement of dask open_kwargs = dict(engine=engine, chunks=chunks or {}, **kwargs)

if parallel:
    import dask

    # wrap the open_dataset, getattr, and preprocess with delayed
    open_ = dask.delayed(open_dataset)
    getattr_ = dask.delayed(getattr)
    if preprocess is not None:
        preprocess = dask.delayed(preprocess)
else:
    open_ = open_dataset
    getattr_ = getattr

datasets = [open_(p, **open_kwargs) for p in paths]
closers = [getattr_(ds, "_close") for ds in datasets]

```

Even if i set the chunks=None, it will be a error cause the chunks always not be None when it is passed into open_dataset .

I think maybe we can keep the chunks value and if anyone want change it, he or she can set it to {} or any other values as they want ?

python open_kwargs = dict(engine=engine, chunks=chunks, **kwargs)

Or may you have a better solution for my problem ?

Also, Thank You for your great jobs on this excellent package.