diff --git a/xarray/core/dataarray.py b/xarray/core/dataarray.py
index b5bcc255b70..0341d84b661 100644
--- a/xarray/core/dataarray.py
+++ b/xarray/core/dataarray.py
@@ -87,7 +87,6 @@
         DatetimeUnitOptions,
         ErrorOptions,
         ErrorOptionsWithWarn,
-        InterpAllOptions,
         InterpOptions,
         PadModeOptions,
         PadReflectOptions,
@@ -2626,7 +2625,7 @@ def fillna(self: T_DataArray, value: Any) -> T_DataArray:
     def interpolate_na(
         self: T_DataArray,
         dim: Hashable | None = None,
-        method: InterpAllOptions = "linear",
+        method: InterpOptions = "linear",
         limit: int | None = None,
         use_coordinate: bool | str = True,
         max_gap: (
diff --git a/xarray/core/dataset.py b/xarray/core/dataset.py
index c5c727f4bed..084437e102c 100644
--- a/xarray/core/dataset.py
+++ b/xarray/core/dataset.py
@@ -109,6 +109,7 @@
         CompatOptions,
         ErrorOptions,
         ErrorOptionsWithWarn,
+        InterpOptions,
         JoinOptions,
         PadModeOptions,
         PadReflectOptions,
@@ -3037,7 +3038,7 @@ def _reindex(
     def interp(
         self,
         coords: Mapping[Any, Any] = None,
-        method: str = "linear",
+        method: InterpOptions = "linear",
         assume_sorted: bool = False,
         kwargs: Mapping[str, Any] = None,
         method_non_numeric: str = "nearest",
@@ -3298,7 +3299,7 @@ def _validate_interp_indexer(x, new_x):
     def interp_like(
         self,
         other: Dataset | DataArray,
-        method: str = "linear",
+        method: InterpOptions = "linear",
         assume_sorted: bool = False,
         kwargs: Mapping[str, Any] = None,
         method_non_numeric: str = "nearest",
diff --git a/xarray/core/missing.py b/xarray/core/missing.py
index 2e869dbe675..5e954c8ce27 100644
--- a/xarray/core/missing.py
+++ b/xarray/core/missing.py
@@ -4,7 +4,7 @@
 import warnings
 from functools import partial
 from numbers import Number
-from typing import TYPE_CHECKING, Any, Callable, Hashable, Sequence
+from typing import TYPE_CHECKING, Any, Callable, Hashable, Sequence, get_args
 
 import numpy as np
 import pandas as pd
@@ -16,6 +16,7 @@
 from .duck_array_ops import datetime_to_numeric, push, timedelta_to_numeric
 from .options import OPTIONS, _get_keep_attrs
 from .pycompat import dask_version, is_duck_dask_array
+from .types import Interp1dOptions, InterpOptions
 from .utils import OrderedSet, is_scalar
 from .variable import Variable, broadcast_variables
 
@@ -309,7 +310,7 @@ def interp_na(
     self,
     dim: Hashable = None,
     use_coordinate: bool | str = True,
-    method: str = "linear",
+    method: InterpOptions = "linear",
     limit: int = None,
     max_gap: int | float | str | pd.Timedelta | np.timedelta64 | dt.timedelta = None,
     keep_attrs: bool = None,
@@ -469,28 +470,20 @@ def _import_interpolant(interpolant, method):
         raise ImportError(f"Interpolation with method {method} requires scipy.") from e
 
 
-def _get_interpolator(method, vectorizeable_only=False, **kwargs):
+def _get_interpolator(
+    method: InterpOptions, vectorizeable_only: bool = False, **kwargs
+):
     """helper function to select the appropriate interpolator class
 
     returns interpolator class and keyword arguments for the class
     """
-    interp1d_methods = [
-        "linear",
-        "nearest",
-        "zero",
-        "slinear",
-        "quadratic",
-        "cubic",
-        "polynomial",
-    ]
-    valid_methods = interp1d_methods + [
-        "barycentric",
-        "krog",
-        "pchip",
-        "spline",
-        "akima",
+    interp_class: type[NumpyInterpolator] | type[ScipyInterpolator] | type[
+        SplineInterpolator
     ]
 
+    interp1d_methods = get_args(Interp1dOptions)
+    valid_methods = tuple(vv for v in get_args(InterpOptions) for vv in get_args(v))
+
     # prioritize scipy.interpolate
     if (
         method == "linear"
@@ -597,7 +590,7 @@ def _floatize_x(x, new_x):
     return x, new_x
 
 
-def interp(var, indexes_coords, method, **kwargs):
+def interp(var, indexes_coords, method: InterpOptions, **kwargs):
     """Make an interpolation of Variable
 
     Parameters
@@ -650,7 +643,7 @@ def interp(var, indexes_coords, method, **kwargs):
         result = Variable(new_dims, interped, attrs=var.attrs)
 
         # dimension of the output array
-        out_dims = OrderedSet()
+        out_dims: OrderedSet = OrderedSet()
         for d in var.dims:
             if d in dims:
                 out_dims.update(indexes_coords[d][1].dims)
@@ -660,7 +653,7 @@ def interp(var, indexes_coords, method, **kwargs):
     return result
 
 
-def interp_func(var, x, new_x, method, kwargs):
+def interp_func(var, x, new_x, method: InterpOptions, kwargs):
     """
     multi-dimensional interpolation for array-like. Interpolated axes should be
     located in the last position.
diff --git a/xarray/core/types.py b/xarray/core/types.py
index 38d13c07c3c..f4f86bafc93 100644
--- a/xarray/core/types.py
+++ b/xarray/core/types.py
@@ -49,11 +49,11 @@
 ]
 JoinOptions = Literal["outer", "inner", "left", "right", "exact", "override"]
 
-InterpOptions = Literal["linear", "nearest", "zero", "slinear", "quadratic", "cubic"]
-Interp1dOptions = Union[InterpOptions, Literal["polynomial"]]
-InterpAllOptions = Union[
-    Interp1dOptions, Literal["barycentric", "krog", "pchip", "spline", "akima"]
+Interp1dOptions = Literal[
+    "linear", "nearest", "zero", "slinear", "quadratic", "cubic", "polynomial"
 ]
+InterpolantOptions = Literal["barycentric", "krog", "pchip", "spline", "akima"]
+InterpOptions = Union[Interp1dOptions, InterpolantOptions]
 
 DatetimeUnitOptions = Literal[
     "Y", "M", "W", "D", "h", "m", "s", "ms", "us", "ns", "ps", "fs", "as"