diff --git a/pangeo_forge/recipe.py b/pangeo_forge/recipe.py
index b07f0b18f..96a809668 100644
--- a/pangeo_forge/recipe.py
+++ b/pangeo_forge/recipe.py
@@ -132,6 +132,7 @@ class NetCDFtoZarrRecipe(BaseRecipe):
     :param sequence_dim: The dimension name along which the inputs will be concatenated.
     :param inputs_per_chunk: The number of inputs to use in each chunk.
     :param nitems_per_input: The length of each input along the `sequence_dim` dimension.
+    :param processed_input_nb: The number of inputs already used to generate the existing dataset.
     :param target: A location in which to put the dataset. Can also be assigned at run time.
     :param input_cache: A location in which to cache temporary data.
     :param require_cache: Whether to allow opening inputs directly which have not
@@ -154,6 +155,7 @@ class NetCDFtoZarrRecipe(BaseRecipe):
     sequence_dim: Optional[str] = None
     inputs_per_chunk: int = 1
     nitems_per_input: int = 1
+    processed_input_nb: int = 0
     target: Optional[AbstractTarget] = field(default_factory=UninitializedTarget)
     input_cache: Optional[AbstractTarget] = field(default_factory=UninitializedTarget)
     require_cache: bool = True
@@ -436,16 +438,18 @@ class NetCDFtoZarrMultiVarSequentialRecipe(NetCDFtoZarrRecipe):
     """There are muliples sequences of input files (but all along the same dimension.)
     Different variables live in different files.
 
+    :param processed_input_urls: The inputs already used to generate the existing dataset.
     :param input_pattern: An pattern used to generate the input file names.
     """
 
+    processed_input_urls: Iterable[str] = field(repr=False, default_factory=list)
     input_pattern: VariableSequencePattern = field(default_factory=VariableSequencePattern)
 
     def __post_init__(self):
         super().__post_init__()
         self._variables = self.input_pattern.keys["variable"]
-        self.processed_input_nb = 0  # TODO
-        self._inputs = {k: {"url": v, "processed": False} for k, v in self.input_pattern}
+        self.processed_input_nb = len(self.processed_input_urls) // len(self._variables)
+        self._inputs = {k: {"url": v, "processed": v in self.processed_input_urls} for k, v in self.input_pattern}
         # input keys are tuples like
         #  ("temp", 0)
         #  ("temp", 1)
diff --git a/tests/test_recipe.py b/tests/test_recipe.py
index 8ebf3ee24..d97318220 100644
--- a/tests/test_recipe.py
+++ b/tests/test_recipe.py
@@ -200,3 +200,42 @@ def test_NetCDFtoZarrMultiVarSequentialRecipe(
 
     ds_target = xr.open_zarr(tmp_target.get_mapper(), consolidated=True).compute()
     assert ds_target.identical(daily_xarray_dataset)
+
+
+def test_NetCDFtoZarrMultiVarSequentialRecipeIncremental(
+    daily_xarray_dataset, netcdf_local_paths_by_variable, tmp_target, tmp_cache
+):
+    paths, items_per_file, fnames_by_variable, path_format = netcdf_local_paths_by_variable
+    pattern1 = VariableSequencePattern(
+        path_format, keys={"variable": ["foo", "bar"], "n": list(range(len(paths) // 4))}
+    )
+    r1 = recipe.NetCDFtoZarrMultiVarSequentialRecipe(
+        input_pattern=pattern1,
+        sequence_dim="time",
+        inputs_per_chunk=1,
+        nitems_per_input=items_per_file,
+        target=tmp_target,
+        input_cache=tmp_cache,
+    )
+
+    processed_input_urls = [v for k, v in pattern1]
+    pattern2 = VariableSequencePattern(
+        path_format, keys={"variable": ["foo", "bar"], "n": list(range(len(paths) // 2))}
+    )
+    r2 = recipe.NetCDFtoZarrMultiVarSequentialRecipe(
+        processed_input_urls=processed_input_urls,
+        input_pattern=pattern2,
+        sequence_dim="time",
+        inputs_per_chunk=1,
+        nitems_per_input=items_per_file,
+        target=tmp_target,
+        input_cache=tmp_cache,
+    )
+    # check that r2 needs r1 to be executed first
+    with pytest.raises(FileNotFoundError):
+        _manually_execute_recipe(r2)
+    _manually_execute_recipe(r1)
+    _manually_execute_recipe(r2)
+
+    ds_target = xr.open_zarr(tmp_target.get_mapper(), consolidated=True).compute()
+    assert ds_target.identical(daily_xarray_dataset)