diff --git a/cwms/timeseries/timeseries.py b/cwms/timeseries/timeseries.py
index 6fdc407..3be32b9 100644
--- a/cwms/timeseries/timeseries.py
+++ b/cwms/timeseries/timeseries.py
@@ -233,6 +233,14 @@ def combine_timeseries_results(results: List[Data]) -> Data:
     )
     combined_df["date-time"] = combined_df["date-time"].astype("Int64")
     combined_df = combined_df.reindex(columns=["date-time", "value", "quality-code"])
+
+    # Replace NaN in value column with None so they serialize as JSON null
+    # rather than the invalid JSON literal NaN.
+    combined_df["value"] = (
+        combined_df["value"]
+        .astype(object)
+        .where(combined_df["value"].notna(), other=None)
+    )
     # Update the "values" key in the JSON to include the combined data
     combined_json["values"] = combined_df.values.tolist()
 
@@ -438,8 +446,11 @@ def timeseries_df_to_json(
         pd.Timestamp.isoformat
     )
     df = df.reindex(columns=["date-time", "value", "quality-code"])
-    if df.isnull().values.any():
-        raise ValueError("Null/NaN data must be removed from the dataframe")
+
+    # Replace NaN/NA/NaT in value column with None so they serialize as JSON
+    # null rather than the invalid JSON literal NaN.
+    df["value"] = df["value"].astype(object).where(df["value"].notna(), other=None)
+
     if version_date:
         version_date_iso = version_date.isoformat()
     else:
diff --git a/pyproject.toml b/pyproject.toml
index 64a8fbd..1e59469 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -2,8 +2,7 @@
 name = "cwms-python"
 repository = "https://github.com/HydrologicEngineeringCenter/cwms-python"
 
-version = "1.0.5"
-
+version = "1.0.6"
 
 packages = [
     { include = "cwms" },
diff --git a/tests/cda/timeseries/timeseries_CDA_test.py b/tests/cda/timeseries/timeseries_CDA_test.py
index 2c31aba..f4d056e 100644
--- a/tests/cda/timeseries/timeseries_CDA_test.py
+++ b/tests/cda/timeseries/timeseries_CDA_test.py
@@ -18,6 +18,8 @@
 TEST_TSID_CHUNK_MULTI = f"{TEST_LOCATION_ID}.Stage.Inst.15Minutes.0.Raw-Multi-Chunk"
 TEST_TSID_COPY = f"{TEST_LOCATION_ID}.Stage.Inst.15Minutes.0.Raw-Copy"
 TEST_TSID_DELETE = f"{TEST_LOCATION_ID}.Stage.Inst.15Minutes.0.Raw-Delete"
+TEST_TSID_CHUNK_NULLS = f"{TEST_LOCATION_ID}.Stage.Inst.15Minutes.0.Raw-Multi-Nulls"
+TEST_TSID_COPY_NULLS = f"{TEST_LOCATION_ID}.Stage.Inst.15Minutes.0.Raw-Copy-Nulls"
 TS_ID_REV_TEST = TEST_TSID_MULTI.replace("Raw-Multi", "Raw-Rev-Test")
 # Generate 15-minute interval timestamps
 START_DATE_CHUNK_MULTI = datetime(2025, 7, 31, 0, 0, tzinfo=timezone.utc)
@@ -31,6 +33,8 @@
     TEST_TSID_STORE,
     TEST_TSID_CHUNK_MULTI,
     TEST_TSID_COPY,
+    TEST_TSID_CHUNK_NULLS,
+    TEST_TSID_COPY_NULLS,
 ]
 
 
@@ -48,6 +52,12 @@
         "quality-code": [0] * len(DT_CHUNK_MULTI),
     }
 )
+# Create a copy of the original DataFrame and introduce null values
+DF_WITH_NULLS = DF_CHUNK_MULTI.copy()
+# Set the 100 and 200 index value to null
+DF_WITH_NULLS.loc[100, "value"] = None
+DF_WITH_NULLS.loc[200, "value"] = None
+
 
 DF_MULTI_TIMESERIES1 = pd.DataFrame(
     {
@@ -275,6 +285,58 @@ def test_store_timeseries_chunk_ts():
     ), f"Data frames do not match: original = {DF_CHUNK_MULTI.describe()}, stored = {df.describe()}"
 
 
+def test_store_timesereis_chunk_to_with_null_values():
+    # Define parameters
+    ts_id = TEST_TSID_CHUNK_NULLS
+    office = TEST_OFFICE
+    units = "m"
+
+    # Convert DataFrame to JSON format
+    ts_json = ts.timeseries_df_to_json(DF_WITH_NULLS, ts_id, units, office)
+
+    ts.store_timeseries(ts_json, multithread=True)
+
+    data_nulls = ts.get_timeseries(
+        ts_id=ts_id,
+        office_id=TEST_OFFICE,
+        begin=START_DATE_CHUNK_MULTI,
+        end=END_DATE_CHUNK_MULTI,
+        unit="SI",
+    )
+    df_nulls = data_nulls.df
+    # make sure the dataframe matches stored dataframe with null values
+    pdt.assert_frame_equal(
+        df_nulls, DF_WITH_NULLS
+    ), f"Data frames do not match: original with nulls = {DF_WITH_NULLS.describe()}, stored = {df_nulls.describe()}"
+
+
+def test_copy_timeseries_chunk_json_with_nulls():
+    data_json = ts.get_timeseries(
+        ts_id=TEST_TSID_CHUNK_NULLS,
+        office_id=TEST_OFFICE,
+        begin=START_DATE_CHUNK_MULTI,
+        end=END_DATE_CHUNK_MULTI,
+        max_days_per_chunk=14,
+        unit="SI",
+    ).json
+    data_json["name"] = TEST_TSID_COPY_NULLS
+    ts.store_timeseries(data_json)
+
+    data_multithread = ts.get_timeseries(
+        ts_id=TEST_TSID_COPY_NULLS,
+        office_id=TEST_OFFICE,
+        begin=START_DATE_CHUNK_MULTI,
+        end=END_DATE_CHUNK_MULTI,
+        max_days_per_chunk=14,
+        unit="SI",
+    )
+    df = data_multithread.df
+    # make sure the dataframe matches stored dataframe with null values
+    pdt.assert_frame_equal(
+        df, DF_WITH_NULLS
+    ), f"Data frames do not match: original with nulls = {DF_WITH_NULLS.describe()}, stored = {df.describe()}"
+
+
 def test_copy_timeseries_chunk_json():
     data_json = ts.get_timeseries(
         ts_id=TEST_TSID_CHUNK_MULTI,