diff --git a/doc/source/whatsnew/v1.6.0.rst b/doc/source/whatsnew/v1.6.0.rst
index 098750aa3a2b2..461ef370c7c88 100644
--- a/doc/source/whatsnew/v1.6.0.rst
+++ b/doc/source/whatsnew/v1.6.0.rst
@@ -117,9 +117,9 @@ Other API changes
 ^^^^^^^^^^^^^^^^^
 - Passing ``nanoseconds`` greater than 999 or less than 0 in :class:`Timestamp` now raises a ``ValueError`` (:issue:`48538`, :issue:`48255`)
 - :func:`read_csv`: specifying an incorrect number of columns with ``index_col`` of now raises ``ParserError`` instead of ``IndexError`` when using the c parser.
+- :meth:`DataFrame.astype`, :meth:`Series.astype`, and :meth:`DatetimeIndex.astype` casting datetime64 data to any of "datetime64[s]", "datetime64[ms]", "datetime64[us]" will return an object with the given resolution instead of coercing back to "datetime64[ns]" (:issue:`48928`)
 -
 
-
 .. ---------------------------------------------------------------------------
 .. _whatsnew_160.deprecations:
 
diff --git a/pandas/core/dtypes/astype.py b/pandas/core/dtypes/astype.py
index 6d04dd755dbfd..892b53a261b26 100644
--- a/pandas/core/dtypes/astype.py
+++ b/pandas/core/dtypes/astype.py
@@ -15,7 +15,11 @@
 import numpy as np
 
 from pandas._libs import lib
-from pandas._libs.tslibs import is_unitless
+from pandas._libs.tslibs import (
+    get_unit_from_dtype,
+    is_supported_unit,
+    is_unitless,
+)
 from pandas._libs.tslibs.timedeltas import array_to_timedelta64
 from pandas._typing import (
     ArrayLike,
@@ -289,11 +293,11 @@ def astype_array_safe(
         and dtype.kind == "M"
         and not is_unitless(dtype)
         and not is_dtype_equal(dtype, values.dtype)
+        and not is_supported_unit(get_unit_from_dtype(dtype))
     ):
-        # unit conversion, we would re-cast to nanosecond, so this is
-        #  effectively just a copy (regardless of copy kwd)
-        # TODO(2.0): remove special-case
-        return values.copy()
+        # Supported units we handle in DatetimeArray.astype; but that raises
+        #  on non-supported units, so we handle that here.
+        return np.asarray(values).astype(dtype)
 
     try:
         new_values = astype_array(values, dtype, copy=copy)
diff --git a/pandas/tests/frame/methods/test_astype.py b/pandas/tests/frame/methods/test_astype.py
index 6d343de9f5d3a..7e7ce4321fb64 100644
--- a/pandas/tests/frame/methods/test_astype.py
+++ b/pandas/tests/frame/methods/test_astype.py
@@ -414,10 +414,56 @@ def test_astype_to_datetime_unit(self, unit):
         dtype = f"M8[{unit}]"
         arr = np.array([[1, 2, 3]], dtype=dtype)
         df = DataFrame(arr)
+        ser = df.iloc[:, 0]
+        idx = pd.Index(ser)
+        dta = ser._values
+
         result = df.astype(dtype)
-        expected = DataFrame(arr.astype(dtype))
 
-        tm.assert_frame_equal(result, expected)
+        if unit in ["ns", "us", "ms", "s"]:
+            # GH#48928
+            exp_dtype = dtype
+        else:
+            # TODO(2.0): use the nearest supported dtype (i.e. M8[s]) instead
+            #  of nanos
+            exp_dtype = "M8[ns]"
+        # TODO(2.0): once DataFrame constructor doesn't cast ndarray inputs.
+        #  can simplify this
+        exp_values = arr.astype(exp_dtype)
+        exp_dta = pd.core.arrays.DatetimeArray._simple_new(
+            exp_values, dtype=exp_values.dtype
+        )
+        exp_df = DataFrame(exp_dta)
+        assert (exp_df.dtypes == exp_dtype).all()
+
+        tm.assert_frame_equal(result, exp_df)
+
+        # TODO(2.0): make Series/DataFrame raise like Index and DTA?
+        res_ser = ser.astype(dtype)
+        exp_ser = exp_df.iloc[:, 0]
+        assert exp_ser.dtype == exp_dtype
+        tm.assert_series_equal(res_ser, exp_ser)
+
+        if unit in ["ns", "us", "ms", "s"]:
+            exp_dta = exp_ser._values
+
+            res_index = idx.astype(dtype)
+            # TODO(2.0): should be able to just call pd.Index(exp_ser)
+            exp_index = pd.DatetimeIndex._simple_new(exp_dta, name=idx.name)
+            assert exp_index.dtype == exp_dtype
+            tm.assert_index_equal(res_index, exp_index)
+
+            res_dta = dta.astype(dtype)
+            assert exp_dta.dtype == exp_dtype
+            tm.assert_extension_array_equal(res_dta, exp_dta)
+        else:
+            msg = rf"Cannot cast DatetimeIndex to dtype datetime64\[{unit}\]"
+            with pytest.raises(TypeError, match=msg):
+                idx.astype(dtype)
+
+            msg = rf"Cannot cast DatetimeArray to dtype datetime64\[{unit}\]"
+            with pytest.raises(TypeError, match=msg):
+                dta.astype(dtype)
 
     @pytest.mark.parametrize("unit", ["ns"])
     def test_astype_to_timedelta_unit_ns(self, unit):