diff --git a/CHANGELOG.md b/CHANGELOG.md
index 0121e67e4441..690c50baf993 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -46,6 +46,8 @@ Remember to align the itemized text with the first line of an item within a list
   * The {func}`jax.numpy.hypot` function now issues a deprecation warning when
     passing complex-valued inputs to it. This will raise an error when the
     deprecation is completed.
+  * Scalar arguments to {func}`jax.numpy.nonzero`, {func}`jax.numpy.where`, and
+    related functions now raise an error, following a similar change in NumPy.
 
 ## jaxlib 0.4.27
 
diff --git a/jax/_src/numpy/lax_numpy.py b/jax/_src/numpy/lax_numpy.py
index 080d67591d3f..3767633deefc 100644
--- a/jax/_src/numpy/lax_numpy.py
+++ b/jax/_src/numpy/lax_numpy.py
@@ -1454,10 +1454,8 @@ def nonzero(a: ArrayLike, *, size: int | None = None,
   arr = asarray(a)
   del a
   if ndim(arr) == 0:
-    # Added 2023 Dec 6
-    warnings.warn("Calling nonzero on 0d arrays is deprecated. Use `atleast_1d(arr).nonzero()",
-                  DeprecationWarning, stacklevel=2)
-  arr = atleast_1d(arr)
+    raise ValueError("Calling nonzero on 0d arrays is not allowed. "
+                     "Use jnp.atleast_1d(scalar).nonzero() instead.")
   mask = arr if arr.dtype == bool else (arr != 0)
   calculated_size = mask.sum() if size is None else size
   calculated_size = core.concrete_dim_or_error(calculated_size,
diff --git a/tests/lax_numpy_test.py b/tests/lax_numpy_test.py
index 39cb1c8532b1..ddc599792e63 100644
--- a/tests/lax_numpy_test.py
+++ b/tests/lax_numpy_test.py
@@ -323,17 +323,15 @@ def testCountNonzero(self, shape, dtype, axis):
     self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, check_dtypes=False)
     self._CompileAndCheck(jnp_fun, args_maker)
 
-  @jtu.sample_product(shape=all_shapes, dtype=all_dtypes)
+  @jtu.sample_product(shape=nonzerodim_shapes, dtype=all_dtypes)
   def testNonzero(self, shape, dtype):
     rng = jtu.rand_some_zero(self.rng())
     args_maker = lambda: [rng(shape, dtype)]
-    with jtu.ignore_warning(category=DeprecationWarning,
-                            message="Calling nonzero on 0d arrays.*"):
-      self._CheckAgainstNumpy(np.nonzero, jnp.nonzero, args_maker, check_dtypes=False)
+    self._CheckAgainstNumpy(np.nonzero, jnp.nonzero, args_maker, check_dtypes=False)
 
   @jtu.sample_product(
     [dict(shape=shape, fill_value=fill_value)
-      for shape in nonempty_array_shapes
+      for shape in nonempty_nonscalar_array_shapes
       for fill_value in [None, -1, shape or (1,)]
      ],
     dtype=all_dtypes,
@@ -351,17 +349,13 @@ def np_fun(x):
         return tuple(np.concatenate([arg, np.full(size - len(arg), fval, arg.dtype)])
                      for fval, arg in safe_zip(fillvals, result))
     jnp_fun = lambda x: jnp.nonzero(x, size=size, fill_value=fill_value)
-    with jtu.ignore_warning(category=DeprecationWarning,
-                            message="Calling nonzero on 0d arrays.*"):
-      self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, check_dtypes=False)
-      self._CompileAndCheck(jnp_fun, args_maker)
+    self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, check_dtypes=False)
+    self._CompileAndCheck(jnp_fun, args_maker)
 
-  @jtu.sample_product(shape=all_shapes, dtype=all_dtypes)
+  @jtu.sample_product(shape=nonzerodim_shapes, dtype=all_dtypes)
   def testFlatNonzero(self, shape, dtype):
     rng = jtu.rand_some_zero(self.rng())
-    np_fun = jtu.ignore_warning(
-      category=DeprecationWarning,
-      message="Calling nonzero on 0d arrays.*")(np.flatnonzero)
+    np_fun = np.flatnonzero
     jnp_fun = jnp.flatnonzero
     args_maker = lambda: [rng(shape, dtype)]
     self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, check_dtypes=False)
@@ -371,7 +365,7 @@ def testFlatNonzero(self, shape, dtype):
     self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
-    shape=nonempty_array_shapes,
+    shape=nonempty_nonscalar_array_shapes,
     dtype=all_dtypes,
     fill_value=[None, -1, 10, (-1,), (10,)],
     size=[1, 5, 10],
@@ -379,7 +373,6 @@ def testFlatNonzero(self, shape, dtype):
   def testFlatNonzeroSize(self, shape, dtype, size, fill_value):
     rng = jtu.rand_some_zero(self.rng())
     args_maker = lambda: [rng(shape, dtype)]
-    @jtu.ignore_warning(category=DeprecationWarning, message="Calling nonzero on 0d arrays.*")
     def np_fun(x):
       result = np.flatnonzero(x)
       if size <= len(result):
@@ -391,24 +384,20 @@ def np_fun(x):
     self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, check_dtypes=False)
     self._CompileAndCheck(jnp_fun, args_maker)
 
-  @jtu.sample_product(shape=all_shapes, dtype=all_dtypes)
+  @jtu.sample_product(shape=nonzerodim_shapes, dtype=all_dtypes)
   def testArgWhere(self, shape, dtype):
     rng = jtu.rand_some_zero(self.rng())
     args_maker = lambda: [rng(shape, dtype)]
-    with jtu.ignore_warning(category=DeprecationWarning,
-                            message="Calling nonzero on 0d arrays.*"):
-      self._CheckAgainstNumpy(np.argwhere, jnp.argwhere, args_maker, check_dtypes=False)
+    self._CheckAgainstNumpy(np.argwhere, jnp.argwhere, args_maker, check_dtypes=False)
 
     # JIT compilation requires specifying a size statically. Full test of this
     # behavior is in testNonzeroSize().
     jnp_fun = lambda x: jnp.argwhere(x, size=np.size(x) // 2)
-    with jtu.ignore_warning(category=DeprecationWarning,
-                            message="Calling nonzero on 0d arrays.*"):
-      self._CompileAndCheck(jnp_fun, args_maker)
+    self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
     [dict(shape=shape, fill_value=fill_value)
-      for shape in nonempty_array_shapes
+      for shape in nonempty_nonscalar_array_shapes
       for fill_value in [None, -1, shape or (1,)]
      ],
     dtype=all_dtypes,
@@ -427,10 +416,8 @@ def np_fun(x):
                         for fval, arg in safe_zip(fillvals, result.T)]).T
     jnp_fun = lambda x: jnp.argwhere(x, size=size, fill_value=fill_value)
 
-    with jtu.ignore_warning(category=DeprecationWarning,
-                            message="Calling nonzero on 0d arrays.*"):
-      self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, check_dtypes=False)
-      self._CompileAndCheck(jnp_fun, args_maker)
+    self._CheckAgainstNumpy(np_fun, jnp_fun, args_maker, check_dtypes=False)
+    self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
     [dict(np_op=getattr(np, rec.name), jnp_op=getattr(jnp, rec.name),
@@ -4490,24 +4477,20 @@ def args_maker(): return []
     self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
-    shape=all_shapes,
+    shape=nonzerodim_shapes,
     dtype=all_dtypes,
   )
   def testWhereOneArgument(self, shape, dtype):
     rng = jtu.rand_some_zero(self.rng())
     args_maker = lambda: [rng(shape, dtype)]
 
-    with jtu.ignore_warning(category=DeprecationWarning,
-                            message="Calling nonzero on 0d arrays.*"):
-      self._CheckAgainstNumpy(np.where, jnp.where, args_maker, check_dtypes=False)
+    self._CheckAgainstNumpy(np.where, jnp.where, args_maker, check_dtypes=False)
 
     # JIT compilation requires specifying a size statically. Full test of
     # this behavior is in testNonzeroSize().
     jnp_fun = lambda x: jnp.where(x, size=np.size(x) // 2)
 
-    with jtu.ignore_warning(category=DeprecationWarning,
-                            message="Calling nonzero on 0d arrays.*"):
-      self._CompileAndCheck(jnp_fun, args_maker)
+    self._CompileAndCheck(jnp_fun, args_maker)
 
   @jtu.sample_product(
     shapes=filter(_shapes_are_broadcast_compatible,