diff --git a/doc/source/whatsnew/v3.0.0.rst b/doc/source/whatsnew/v3.0.0.rst
index bf3a6745aae3d..1b513c199c672 100644
--- a/doc/source/whatsnew/v3.0.0.rst
+++ b/doc/source/whatsnew/v3.0.0.rst
@@ -1195,6 +1195,7 @@ Indexing
 - Bug in :meth:`Index.get_indexer` and similar methods when ``NaN`` is located at or after position 128 (:issue:`58924`)
 - Bug in :meth:`MultiIndex.insert` when a new value inserted to a datetime-like level gets cast to ``NaT`` and fails indexing (:issue:`60388`)
 - Bug in :meth:`Series.__setitem__` when assigning boolean series with boolean indexer will raise ``LossySetitemError`` (:issue:`57338`)
+- Bug in indexing ``obj.loc[start:stop]`` with a :class:`DatetimeIndex` and :class:`Timestamp` endpoints with higher resolution than the index (:issue:`63262`)
 - Bug in printing :attr:`Index.names` and :attr:`MultiIndex.levels` would not escape single quotes (:issue:`60190`)
 - Bug in reindexing of :class:`DataFrame` with :class:`PeriodDtype` columns in case of consolidated block (:issue:`60980`, :issue:`60273`)
 - Bug in :meth:`DataFrame.loc.__getitem__` and :meth:`DataFrame.iloc.__getitem__` with a :class:`CategoricalDtype` column with integer categories raising when trying to index a row containing a ``NaN`` entry (:issue:`58954`)
diff --git a/pandas/core/indexes/datetimelike.py b/pandas/core/indexes/datetimelike.py
index e81181e49eef7..fd061666c1f00 100644
--- a/pandas/core/indexes/datetimelike.py
+++ b/pandas/core/indexes/datetimelike.py
@@ -11,6 +11,7 @@
 from typing import (
     TYPE_CHECKING,
     Any,
+    Literal,
     Self,
     cast,
     final,
@@ -20,16 +21,18 @@
 
 from pandas._libs import (
     NaT,
-    Timedelta,
     lib,
 )
 from pandas._libs.tslibs import (
     BaseOffset,
     Resolution,
     Tick,
+    Timedelta,
+    Timestamp,
     parsing,
     to_offset,
 )
+from pandas._libs.tslibs.dtypes import abbrev_to_npy_unit
 from pandas.compat.numpy import function as nv
 from pandas.errors import (
     InvalidIndexError,
@@ -933,6 +936,22 @@ def _from_join_target(self, result: np.ndarray):
         result = result.view(self._data._ndarray.dtype)
         return self._data._from_backing_data(result)
 
+    def _searchsorted_monotonic(self, label, side: Literal["left", "right"] = "left"):
+        if (
+            self.is_monotonic_increasing
+            and isinstance(label, (Timestamp, Timedelta))
+            and abbrev_to_npy_unit(label.unit) > abbrev_to_npy_unit(self.unit)
+        ):
+            # For non-matching units we can safely round down (with side=right)
+            # This is needed for GH#63262
+            if side == "right":
+                label = label.as_unit(self.unit)  # this should always be a round-down
+            else:
+                # round up
+                label = label.ceil(self.unit).as_unit(self.unit)
+
+        return super()._searchsorted_monotonic(label, side)
+
     # --------------------------------------------------------------------
     # List-like Methods
 
diff --git a/pandas/tests/indexes/datetimes/test_indexing.py b/pandas/tests/indexes/datetimes/test_indexing.py
index 8bb02563233a4..2f7eaa2c209cd 100644
--- a/pandas/tests/indexes/datetimes/test_indexing.py
+++ b/pandas/tests/indexes/datetimes/test_indexing.py
@@ -656,6 +656,32 @@ def test_maybe_cast_slice_duplicate_monotonic(self):
 
 
 class TestGetSliceBounds:
+    @pytest.mark.parametrize("as_td", [True, False])
+    def test_get_slice_bound_mismatched_unit(self, as_td):
+        # GH#63262
+        index = date_range(start="2000-01-01", freq="h", periods=8)
+
+        td = pd.Timedelta(1)
+        ts = Timestamp("2000-01-01 01:00:00")
+        start = ts - td
+        stop = ts + td
+        if as_td:
+            index = index - Timestamp(0).as_unit("us")
+            start = start - Timestamp(0).as_unit("us")
+            stop = stop - Timestamp(0).as_unit("us")
+
+        left = index.get_slice_bound(start, side="left")
+        assert left == 1
+        right = index.get_slice_bound(stop, side="right")
+        assert right == 2
+
+        # The user-facing behavior is slicing with .loc, so let's test that
+        #  explicitly while we're here.
+        ser = pd.Series(1, index=index)
+        result = ser.loc[start:stop]
+        expected = ser.iloc[1:2]
+        tm.assert_series_equal(result, expected)
+
     @pytest.mark.parametrize("box", [date, datetime, Timestamp])
     @pytest.mark.parametrize("side, expected", [("left", 4), ("right", 5)])
     def test_get_slice_bounds_datetime_within(