id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
656982083,MDU6SXNzdWU2NTY5ODIwODM=,4224,wrong time encoding after padding,8161792,open,0,,,3,2020-07-15T00:46:53Z,2022-04-29T17:39:17Z,,NONE,,,,"<!-- Please include a self-contained copy-pastable example that generates the issue if possible.

Please be concise with code posted. See guidelines below on how to provide a good bug report:

- Craft Minimal Bug Reports: http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports
- Minimal Complete Verifiable Examples: https://stackoverflow.com/help/mcve

Bug reports that follow these guidelines are easier to diagnose, and so are often handled much more quickly.
-->

**What happened**:

If I open a netcdf with default settings (contain a daily time dimension) and then pad with hourly values, even the padded dataset shows hourly time values, the hourly values cannot be saved. I think this is due to the encoding, but I'm not sure how to fix it. 

**What you expected to happen**:
I expected the final line of code give me 
```python
#array(['2000-01-01T00:00:00.000000000', '2000-01-01T01:00:00.000000000',
#      '2000-01-01T02:00:00.000000000', '2000-01-01T03:00:00.000000000',
#       '2000-01-01T04:00:00.000000000'], dtype='datetime64[ns]')
```
Instead, it outputs
```python
#array(['2000-01-01T00:00:00.000000000', '2000-01-01T00:00:00.000000000',
#      '2000-01-01T00:00:00.000000000', '2000-01-01T00:00:00.000000000',
#      '2000-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
```

**Minimal Complete Verifiable Example**:

```python
import xarray as xr

time = pd.date_range(""2000-01-01"", freq=""1D"", periods=365 )
ds = xr.Dataset({""foo"": (""time"", np.arange(365)), ""time"": time})
ds.to_netcdf('test5.nc')

ds = xr.open_dataset('test5.nc')
ds.time.encoding

# padding
ds_hourly = ds.resample(time='1h').pad()
ds_hourly.time.values[0:5]
#array(['2000-01-01T00:00:00.000000000', '2000-01-01T01:00:00.000000000',
#      '2000-01-01T02:00:00.000000000', '2000-01-01T03:00:00.000000000',
#       '2000-01-01T04:00:00.000000000'], dtype='datetime64[ns]')

ds_hourly.to_netcdf('test6.nc')

# load the padded data file
ds_hourly_load = xr.open_dataset('test6.nc')
ds_hourly_load.time.values[0:5]
#array(['2000-01-01T00:00:00.000000000', '2000-01-01T00:00:00.000000000',
#      '2000-01-01T00:00:00.000000000', '2000-01-01T00:00:00.000000000',
#      '2000-01-01T00:00:00.000000000'], dtype='datetime64[ns]')
```

**Anything else we need to know?**:

**Environment**:
xarray version: '0.15.1'
<details><summary>Output of <tt>xr.show_versions()</tt></summary>

<!-- Paste the output here xr.show_versions() here -->


</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4224/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue
668905666,MDU6SXNzdWU2Njg5MDU2NjY=,4291,resample function gives 0s instead of NaNs,8161792,closed,0,,,3,2020-07-30T15:59:32Z,2020-08-05T16:55:58Z,2020-08-05T16:55:58Z,NONE,,,,"<!-- Please include a self-contained copy-pastable example that generates the issue if possible.

Please be concise with code posted. See guidelines below on how to provide a good bug report:

- Craft Minimal Bug Reports: http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports
- Minimal Complete Verifiable Examples: https://stackoverflow.com/help/mcve

Bug reports that follow these guidelines are easier to diagnose, and so are often handled much more quickly.
-->

**What happened**:
When I use `resample(time='1d').sum(dim='time')` to resample a time series with NaNs, the resampled result gives me 0s instead of NaNs, while NaNs should be the correct answer.

**What you expected to happen**:

NaNs should be the correct answer.

**Minimal Complete Verifiable Example**:

```python
import xarray as xr

dates =  pd.date_range('20200101', '20200601', freq='h')
data = np.linspace(0, 10, num=len(dates))
data[0:30*24] = np.nan

da = xr.DataArray(data, coords=[dates], dims='time')
da.plot()

# Instead of NaNs, the resampled time series in January 20202 give us 0s, which not right.
da.resample(time='1d', skipna=True).sum(dim='time', skipna=True).plot()
```

**Anything else we need to know?**:

Did I misunderstand something here? Thanks!


**Environment**:
xarray - '0.15.1' 

<details><summary>Output of <tt>xr.show_versions()</tt></summary>

xarray - '0.15.1' 


</details>
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