How to handle datasets with invalid info[meas_id][secs]?

I'm woking with the ds000246 OpenNeuro dataset:

$ aws s3 sync --no-sign-request s3://openneuro.org/ds000246 ds000246
$ cd ds000246/sub-emptyroom/meg

Reading the data works as expected:

import mne
raw = mne.io.read_raw_ctf('sub-emptyroom_task-noise_run-01_meg.ds')

Writing thows an exception:

raw.save('/tmp/foo.fif')

Traceback:

RuntimeError                              Traceback (most recent call last)
<ipython-input-4-eb369e79ee42> in <module>
----> 1 raw.save('/tmp/foo.fif')

<decorator-gen-155> in save(self, fname, picks, tmin, tmax, buffer_size_sec, drop_small_buffer, proj, fmt, overwrite, split_size, split_naming, verbose)

~/Development/mne-python/mne/io/base.py in save(self, fname, picks, tmin, tmax, buffer_size_sec, drop_small_buffer, proj, fmt, overwrite, split_size, split_naming, verbose)
   1379                 "split_naming must be either 'neuromag' or 'bids' instead "
   1380                 "of '{}'.".format(split_naming))
-> 1381         _write_raw(fname, self, info, picks, fmt, data_type, reset_range,
   1382                    start, stop, buffer_size, projector, drop_small_buffer,
   1383                    split_size, split_naming, part_idx, None, overwrite)

~/Development/mne-python/mne/io/base.py in _write_raw(fname, raw, info, picks, fmt, data_type, reset_range, start, stop, buffer_size, projector, drop_small_buffer, split_size, split_naming, part_idx, prev_fname, overwrite)
   1844 
   1845     picks = _picks_to_idx(info, picks, 'all', ())
-> 1846     fid, cals = _start_writing_raw(use_fname, info, picks, data_type,
   1847                                    reset_range, raw.annotations)
   1848 

~/Development/mne-python/mne/io/base.py in _start_writing_raw(name, info, sel, data_type, reset_range, annotations)
   2018         cals.append(info['chs'][k]['cal'] * info['chs'][k]['range'])
   2019 
-> 2020     write_meas_info(fid, info, data_type=data_type, reset_range=reset_range)
   2021 
   2022     #

~/Development/mne-python/mne/io/meas_info.py in write_meas_info(fid, info, data_type, reset_range)
   1453     """
   1454     info._check_consistency()
-> 1455     _check_dates(info)
   1456 
   1457     # Measurement info

~/Development/mne-python/mne/io/meas_info.py in _check_dates(info, prepend_error)
   1411                 if (value[key_2] < np.iinfo('>i4').min or
   1412                         value[key_2] > np.iinfo('>i4').max):
-> 1413                     raise RuntimeError('%sinfo[%s][%s] must be between '
   1414                                        '"%r" and "%r", got "%r"'
   1415                                        % (prepend_error, key, key_2,

RuntimeError: info[meas_id][secs] must be between "-2147483648" and "2147483647", got "-5364633480"

How to best deal with data like this? Can I simply set info[meas_id][secs] to an arbitrary (valid) value? Also it seems a little odd that I can create (and work with) some data by reading it, but then cannot write it back to disk…

Reproduce the failure with the ds000246 CTF recording using mne.io.read_raw_ctf and raw.save. Read mne/io/base.py around _start_writing_raw and mne/io/meas_info.py around write_meas_info and _check_dates; done means agreeing on the supported handling for invalid info[meas_id][secs] and covering the read/write behavior with a regression test.