diff --git a/pepsico/app_calc.py b/pepsico/app_calc.py
index a1685db6..a8926cdd 100644
--- a/pepsico/app_calc.py
+++ b/pepsico/app_calc.py
@@ -174,6 +174,22 @@ def seasonal_wwc(
             )
         )
         wwc_units = "days"
+    if variable == "longest_dry_spell":
+        data_ds = (
+            (labelled_season_data <= wet_threshold)
+            .where(~np.isnan(labelled_season_data))
+            .groupby(labelled_season_data["seasons_starts"])
+            .map(length_of_longest_spell, "T")
+        )
+        wwc_units = "days"
+    if variable == "longest_wet_spell":
+        data_ds = (
+            (labelled_season_data > wet_threshold)
+            .where(~np.isnan(labelled_season_data))
+            .groupby(labelled_season_data["seasons_starts"])
+            .map(length_of_longest_spell, "T")
+        )
+        wwc_units = "days"
     # This is all a bit tedious but I didn't figure out another way to keep
     # seasons_ends and renaming time dim T
     # Can revisit later if this code has a future
@@ -443,14 +459,17 @@ def _cumsum_flagged_diff(flagged_data, dim):
     # Special case coord.size = 1
     cfd = flagged_data
     if cfd[dim].size != 1:
+        # it's ok to treat individual NaNs as 0s,
+        # but if all NaN then must return NaN
+        cfd_mask = ~(np.isnan(cfd).all(dim=dim))
         # Points to apply diff to
         unflagged_and_ends = (flagged_data == 0) * 1
         unflagged_and_ends[{dim: [0, -1]}] = 1
     
-        cfd = cfd.cumsum(dim=dim).where(unflagged_and_ends, other = np.nan).where(
+        cfd = cfd.cumsum(dim=dim).where(unflagged_and_ends, other=np.nan).where(
             # first cumul point must be set to 0
             lambda x: x[dim] != cfd[dim][0], other=0
-        ).bfill(dim).diff(dim)
+        ).bfill(dim).diff(dim).where(cfd_mask)
     return cfd
 
 def count_days_in_spells(
@@ -515,6 +534,7 @@ def length_of_longest_spell(flagged_data, dim):
 
     return _cumsum_flagged_diff(flagged_data, dim).max(dim=dim)
 
+
 def mean_length_of_spells(flagged_data, dim, min_spell_length=1):
     """ Mean length of spells.
     
diff --git a/pepsico/proj_wwc/layout.py b/pepsico/proj_wwc/layout.py
index b3a4d437..6f1cb77c 100644
--- a/pepsico/proj_wwc/layout.py
+++ b/pepsico/proj_wwc/layout.py
@@ -48,8 +48,8 @@ def app_layout():
                         "frost_days",
                         "Tmax_90",
                         "Tmin_10",
-                        # "longest_dry_spell",
-                        # "longest_wet_spell",
+                        "longest_dry_spell",
+                        "longest_wet_spell",
                         # "wet_day_persistence",
                         # "dry_day_persistence",
                         # "dry_spells_mean_length",
@@ -67,14 +67,14 @@ def app_layout():
                         "Count of Frost Days",
                         "Max Temperature 90th %-ile",
                         "Min Temperature 10th %-ile",
-                        # "Longest Dry Spell",
-                        # "Longest Wet Spell",
+                        "Longest Dry Spell",
+                        "Longest Wet Spell",
                         # "Mean Wet Day Persistence",
                         # "Mean Dry Day Persistence",
                         # "Mean Dry Spells Length",
                         # "Median Dry Spells Length",
                     ],
-                    init=0,
+                    init=1,
                 )),
                 Block("Definitions",
                     "Frost <=",
@@ -234,6 +234,13 @@ def app_layout():
                     temperature is greater than a user-defined thredhold.
                 """
             ]),
+            html.P([
+                html.B("Longest Dry/Wet Spell (longest_dry/wet_spell):"),"""
+                    Length of longest dry/wet spell in the season. A dry/wet spell 
+                    is defined as consecutive dry/wet days. A dry/wet day is defined 
+                    as lesser or equal / greather than a user-defined threshold.
+                """
+            ]),
         ),
         
         lou.map(GLOBAL_CONFIG["zoom"]),
diff --git a/pepsico/proj_wwc/maproom.py b/pepsico/proj_wwc/maproom.py
index 4fea4702..c12ec4f2 100644
--- a/pepsico/proj_wwc/maproom.py
+++ b/pepsico/proj_wwc/maproom.py
@@ -100,8 +100,8 @@ def select_var(variable):
             # "rain_events",
             "dry_days",
             "wet_days",
-            # "longest_dry_spell",
-            # "longest_wet_spell",
+            "longest_dry_spell",
+            "longest_wet_spell",
             "wet_day_persistence",
             "dry_day_persistence",
             "dry_spells_mean_length",
@@ -515,7 +515,7 @@ def map_attributes(data, variable):
             colorscale = CMAPS["prcp_anomaly"]
         if data.name in ["tasmin", "tasmax"]:
             colorscale = CMAPS["temp_anomaly"]
-        if variable in ["frost_days", "dry_days"]:
+        if variable in ["frost_days", "dry_days", "longest_dry_spell"]:
             colorscale = colorscale.reversed()
         map_amp = np.max(np.abs(data)).values
         colorscale = colorscale.rescaled(-1*map_amp, map_amp)
diff --git a/pepsico/test/test_app_calc.py b/pepsico/test/test_app_calc.py
index d3100d3a..d8169698 100644
--- a/pepsico/test/test_app_calc.py
+++ b/pepsico/test/test_app_calc.py
@@ -58,6 +58,19 @@ def test__cumsum_flagged_diff_coord_size_1():
     assert spells0 == 0
 
 
+def test__cumsum_flagged_diff_allnan():
+
+    t = pd.date_range(start="2000-05-01", end="2000-05-10", freq="1D")
+    values = np.array([
+        np.nan, np.nan, np.nan, np.nan, np.nan,
+        np.nan, np.nan, np.nan, np.nan, np.nan,
+    ])
+    precip = xr.DataArray(values, coords={"T": t})
+    spells = app_calc._cumsum_flagged_diff(precip, "T")
+
+    assert np.isnan(spells).all()
+
+
 def test_spells_length():
 
     t = pd.date_range(start="2000-05-01", end="2000-05-10", freq="1D")
@@ -91,6 +104,18 @@ def test_length_of_longest_spell():
 
     assert longest_spell == 3
 
+def test_length_of_longest_spell_allnan():
+
+    t = pd.date_range(start="2000-05-01", end="2000-05-10", freq="1D")
+    values = np.array([
+        np.nan, np.nan, np.nan, np.nan, np.nan,
+        np.nan, np.nan, np.nan, np.nan, np.nan,
+    ])
+    precip = xr.DataArray(values, coords={"T": t})
+    longest_spell = app_calc.length_of_longest_spell(precip, "T")
+
+    assert np.isnan(longest_spell)
+
 def test_mean_length_of_spells():
 
     t = pd.date_range(start="2000-05-01", end="2000-05-10", freq="1D")