diff --git a/xcdat/regridder/regrid2.py b/xcdat/regridder/regrid2.py
index 8a1fdc85..710313d1 100644
--- a/xcdat/regridder/regrid2.py
+++ b/xcdat/regridder/regrid2.py
@@ -1,8 +1,9 @@
-from typing import Any, List, Optional, Tuple
+from typing import Any, Dict, List, Optional, Tuple
 
 import numpy as np
 import xarray as xr
 
+import xcdat as xc
 from xcdat.axis import get_dim_keys
 from xcdat.regridder.base import BaseRegridder, _preserve_bounds
 
@@ -105,8 +106,6 @@ def horizontal(self, data_var: str, ds: xr.Dataset) -> xr.Dataset:
             ds,
             data_var,
             output_data,
-            dst_lat_bnds,
-            dst_lon_bnds,
             self._input_grid,
             self._output_grid,
         )
@@ -228,38 +227,90 @@ def _build_dataset(
     ds: xr.Dataset,
     data_var: str,
     output_data: np.ndarray,
-    dst_lat_bnds,
-    dst_lon_bnds,
     input_grid: xr.Dataset,
     output_grid: xr.Dataset,
 ) -> xr.Dataset:
-    input_data_var = ds[data_var]
+    """Build a new xarray Dataset with the given output data and coordinates.
+
+    Parameters
+    ----------
+    ds : xr.Dataset
+        The input dataset containing the data variable to be regridded.
+    data_var : str
+        The name of the data variable in the input dataset to be regridded.
+    output_data : np.ndarray
+        The regridded data to be included in the output dataset.
+    input_grid : xr.Dataset
+        The input grid dataset containing the original grid information.
+    output_grid : xr.Dataset
+        The output grid dataset containing the new grid information.
 
-    output_coords: dict[str, xr.DataArray] = {}
-    output_data_vars: dict[str, xr.DataArray] = {}
+    Returns
+    -------
+    xr.Dataset
+        A new dataset containing the regridded data variable with updated
+        coordinates and attributes.
+    """
+    dv_input = ds[data_var]
 
-    dims = list(input_data_var.dims)
+    output_coords = _get_output_coords(dv_input, output_grid)
 
     output_da = xr.DataArray(
         output_data,
-        dims=dims,
+        dims=dv_input.dims,
         coords=output_coords,
         attrs=ds[data_var].attrs.copy(),
         name=data_var,
     )
 
-    output_data_vars[data_var] = output_da
-
-    output_ds = xr.Dataset(
-        output_data_vars,
-        attrs=input_grid.attrs.copy(),
-    )
-
+    output_ds = output_da.to_dataset()
+    output_ds.attrs = input_grid.attrs.copy()
     output_ds = _preserve_bounds(ds, output_grid, output_ds, ["X", "Y"])
 
     return output_ds
 
 
+def _get_output_coords(
+    dv_input: xr.DataArray, output_grid: xr.Dataset
+) -> Dict[str, xr.DataArray]:
+    """
+    Generate the output coordinates for regridding based on the input data
+    variable and output grid.
+
+    Parameters
+    ----------
+    dv_input : xr.DataArray
+        The input data variable containing the original coordinates.
+    output_grid : xr.Dataset
+        The dataset containing the target grid coordinates.
+
+    Returns
+    -------
+    Dict[str, xr.DataArray]
+        A dictionary where keys are coordinate names and values are the
+        corresponding coordinates from the output grid or input data variable,
+        aligned with the dimensions of the input data variable.
+    """
+    output_coords: Dict[str, xr.DataArray] = {}
+
+    # First get the X and Y axes from the output grid.
+    for key in ["X", "Y"]:
+        input_coord = xc.get_dim_coords(dv_input, key)  # type: ignore
+        output_coord = xc.get_dim_coords(output_grid, key)  # type: ignore
+
+        output_coords[str(input_coord.name)] = output_coord  # type: ignore
+
+    # Get the remaining axes the input data variable (e.g., "time").
+    for dim in dv_input.dims:
+        if dim not in output_coords:
+            output_coords[str(dim)] = dv_input[dim]
+
+    # Sort the coords to align with the input data variable dims.
+    output_coords = {str(dim): output_coords[str(dim)] for dim in dv_input.dims}
+
+    return output_coords
+
+
 def _map_latitude(
     src: np.ndarray, dst: np.ndarray
 ) -> Tuple[List[np.ndarray], List[np.ndarray]]:
@@ -553,12 +604,15 @@ def _get_dimension(input_data_var, cf_axis_name):
 
 
 def _get_bounds_ensure_dtype(ds, axis):
+    bounds = None
+
     try:
-        name = ds.cf.bounds[axis][0]
-    except (KeyError, IndexError) as e:
-        raise RuntimeError(f"Could not determine {axis!r} bounds") from e
-    else:
-        bounds = ds[name]
+        bounds = ds.bounds.get_bounds(axis)
+    except KeyError:
+        pass
+
+    if bounds is None:
+        raise RuntimeError(f"Could not determine {axis!r} bounds")
 
     if bounds.dtype != np.float32:
         bounds = bounds.astype(np.float32)