diff --git a/atlite/datasets/era5.py b/atlite/datasets/era5.py
index 58a62dda..4e4e2337 100644
--- a/atlite/datasets/era5.py
+++ b/atlite/datasets/era5.py
@@ -86,6 +86,8 @@ def _rename_and_clean_coords(ds, add_lon_lat=True):
     longitude columns as 'lat' and 'lon'.
     """
     ds = ds.rename({"longitude": "x", "latitude": "y"})
+    if "valid_time" in ds.sizes:
+        ds = ds.rename({"valid_time": "time"}).unify_chunks()
     # round coords since cds coords are float32 which would lead to mismatches
     ds = ds.assign_coords(
         x=np.round(ds.x.astype(float), 5), y=np.round(ds.y.astype(float), 5)
@@ -96,10 +98,24 @@ def _rename_and_clean_coords(ds, add_lon_lat=True):
 
     # Combine ERA5 and ERA5T data into a single dimension.
     # See https://github.com/PyPSA/atlite/issues/190
-    if "expver" in ds.dims.keys():
-        # expver=1 is ERA5 data, expver=5 is ERA5T data
-        # This combines both by filling in NaNs from ERA5 data with values from ERA5T.
-        ds = ds.sel(expver=1).combine_first(ds.sel(expver=5))
+    if "expver" in ds.coords:
+        unique_expver = np.unique(ds["expver"].values)
+        if len(unique_expver) > 1:
+            expver_dim = xr.DataArray(
+                unique_expver, dims=["expver"], coords={"expver": unique_expver}
+            )
+            ds = (
+                ds.assign_coords({"expver_dim": expver_dim})
+                .drop_vars("expver")
+                .rename({"expver_dim": "expver"})
+                .set_index(expver="expver")
+            )
+            for var in ds.data_vars:
+                ds[var] = ds[var].expand_dims("expver")
+            # expver=1 is ERA5 data, expver=5 is ERA5T data This combines both
+            # by filling in NaNs from ERA5 data with values from ERA5T.
+            ds = ds.sel(expver="0001").combine_first(ds.sel(expver="0005"))
+    ds = ds.drop_vars(["expver", "number"], errors="ignore")
 
     return ds
 
@@ -316,7 +332,7 @@ def retrieve_data(product, chunks=None, tmpdir=None, lock=None, **updates):
     Download data like ERA5 from the Climate Data Store (CDS).
 
     If you want to track the state of your request go to
-    https://cds.climate.copernicus.eu/cdsapp#!/yourrequests
+    https://cds-beta.climate.copernicus.eu/requests?tab=all
     """
     request = {"product_type": "reanalysis", "format": "netcdf"}
     request.update(updates)