diff --git a/docs/source/recipes/plot_19_recipe.py b/docs/source/recipes/plot_19_recipe.py
new file mode 100644
index 0000000000..bcc8f2823f
--- /dev/null
+++ b/docs/source/recipes/plot_19_recipe.py
@@ -0,0 +1,111 @@
+"""
+Plotting per-season trends in global sea surface tempreature extrema
+====================================================================
+
+In this recipe we find the area-based extrema of global sea surface
+temperature per month and, because it is very difficult to
+interpret for trends when in a monthly form, we calculate and plot
+on top of this the mean across each season for both the minima and the
+maxima.
+"""
+
+# %%
+# 1. Import cf-python, cf-plot and other required packages:
+import cfplot as cfp
+import matplotlib.pyplot as plt
+
+import cf
+
+# %%
+# 2. Read the dataset in extract the SST Field from the FieldList:
+f = cf.read("~/recipes/ERA5_monthly_averaged_SST.nc")
+sst = f[0]  # this gives the sea surface temperature (SST)
+
+# %%
+# 3. Collapse the SST data by area extrema (extrema over spatial dimensions):
+am_max = sst.collapse("area: maximum")  # equivalent to "X Y: maximum"
+am_min = sst.collapse("area: minimum")  # equivalent to "X Y: minimum"
+
+# %%
+# 4. Reduce all timeseries down to just 1980+ since there are some data
+# quality issues before 1970 and also this window is about perfect size
+# for viewing the trends without the line plot becoming too cluttered:
+am_max = am_max.subspace(T=cf.ge(cf.dt("1980-01-01")))
+am_min = am_min.subspace(T=cf.ge(cf.dt("1980-01-01")))
+
+# %%
+# 5. Create a mapping which provides the queries we need to collapse on
+# the four seasons, along with our description of them, as a value, with
+# the key of the string encoding the colour we want to plot these
+# trendlines in. This structure will be iterated over to make our plot:
+colours_seasons_mapping = {
+    "red": (cf.mam(), "Mean across MAM: March, April and May"),
+    "blue": (cf.jja(), "Mean across JJA: June, July and August"),
+    "green": (cf.son(), "Mean across SON: September, October and November"),
+    "purple": (cf.djf(), "Mean across DJF: December, January and February"),
+}
+
+# %%
+# 6. Create and open the plot file:
+cfp.gopen(
+    rows=2, columns=1, bottom=0.1, top=0.85, file="global_avg_sst_plot.png"
+)
+
+# %%
+# 7. Put maxima subplot at top since these values are higher, given
+# increasing x axis. Note we set limits manually with 'gset' only to
+# allow space so the legend doesn't overlap the data, which isn't
+# possible purely from positioning it anywhere within the default plot.
+# Otherwise cf-plot handles this for us. To plot the per-season means
+# of the maxima, we loop through the season query mapping and do a
+# "T: mean" collapse setting the season as the grouping:
+cfp.gpos(1)
+cfp.gset(xmin="1980-01-01", xmax="2022-12-01", ymin=304, ymax=312)
+for colour, season_query in colours_seasons_mapping.items():
+    query_on_season, season_description = season_query
+    am_max_collapse = am_max.collapse("T: mean", group=query_on_season)
+    cfp.lineplot(
+        am_max_collapse,
+        color=colour,
+        markeredgecolor=colour,
+        marker="o",
+        label=season_description,
+        title="Maxima per month or season",
+    )
+cfp.lineplot(
+    am_max,
+    color="grey",
+    xlabel="",
+    label="All months",
+)
+
+# %%
+# 8. Create and add minima subplot below the maxima one. Just like for the
+# maxima case, we plot per-season means by looping through the season query
+# mapping and doing a "T: mean" collapse setting the season as the grouping:
+cfp.gpos(2)
+cfp.gset(xmin="1980-01-01", xmax="2022-12-01", ymin=269, ymax=272)
+for colour, season_query in colours_seasons_mapping.items():
+    query_on_season, season_description = season_query
+    am_min_collapse = am_min.collapse("T: mean", group=query_on_season)
+    cfp.lineplot(
+        am_min_collapse,
+        color=colour,
+        markeredgecolor=colour,
+        marker="o",
+        xlabel="",
+        title="Minima per month or season",
+    )
+cfp.lineplot(
+    am_min,
+    color="grey",
+)
+
+# %%
+# 9. Add an overall title to the plot and close the file to save it:
+plt.suptitle(
+    "Global mean sea surface temperature (SST) monthly\nminima and maxima "
+    "showing seasonal means of these extrema",
+    fontsize=18,
+)
+cfp.gclose()
diff --git a/docs/source/recipes/recipe_list.txt b/docs/source/recipes/recipe_list.txt
index 3bfaafb314..813b4e09a3 100644
--- a/docs/source/recipes/recipe_list.txt
+++ b/docs/source/recipes/recipe_list.txt
@@ -34,3 +34,5 @@ plot_17_recipe.html#sphx-glr-recipes-plot-17-recipe-py
 <div class="sphx-glr-thumbcontainer contourmap subspace subplot" tooltip="Contourmap, Subspace, Subplot">
 plot_18_recipe.html#sphx-glr-recipes-plot-18-recipe-py
 <div class="sphx-glr-thumbcontainer regrid stats" tooltip="Regrid, Statistical Operations">
+plot_19_recipe.html#sphx-glr-recipes-plot-19-recipe-py
+<div class="sphx-glr-thumbcontainer collapse subspace subplot lineplot" tooltip="Collapse, Subspace, Subplot, Lineplot">
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