diff --git a/CHANGELOG.md b/CHANGELOG.md
index 26b71522..4305b870 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -10,6 +10,8 @@ Keep it human-readable, your future self will thank you!
 
 ## [Unreleased](https://github.com/ecmwf/anemoi-training/compare/0.3.0...HEAD)
 ### Fixed
+- Update `n_pixel` used by datashader to better adapt across resolutions #152
+
 
 - Fixed bug in power spectra plotting for the n320 resolution.
 - Allow histogram and spectrum plot for one variable [#165](https://github.com/ecmwf/anemoi-training/pull/165)
diff --git a/src/anemoi/training/diagnostics/plots.py b/src/anemoi/training/diagnostics/plots.py
index 93e2d324..45818b69 100644
--- a/src/anemoi/training/diagnostics/plots.py
+++ b/src/anemoi/training/diagnostics/plots.py
@@ -24,6 +24,7 @@
 from matplotlib.collections import PathCollection
 from matplotlib.colors import BoundaryNorm
 from matplotlib.colors import ListedColormap
+from matplotlib.colors import Normalize
 from matplotlib.colors import TwoSlopeNorm
 from pyshtools.expand import SHGLQ
 from pyshtools.expand import SHExpandGLQ
@@ -568,8 +569,12 @@ def error_plot_in_degrees(array1: np.ndarray, array2: np.ndarray) -> np.ndarray:
             datashader=datashader,
         )
     else:
-        single_plot(fig, ax[1], lon, lat, truth, title=f"{vname} target", datashader=datashader)
-        single_plot(fig, ax[2], lon, lat, pred, title=f"{vname} pred", datashader=datashader)
+        combined_data = np.concatenate((input_, truth, pred))
+        # For 'errors', only persistence and increments need identical colorbar-limits
+        combined_error = np.concatenate(((pred - input_), (truth - input_)))
+        norm = Normalize(vmin=np.nanmin(combined_data), vmax=np.nanmax(combined_data))
+        single_plot(fig, ax[1], lon, lat, truth, norm=norm, title=f"{vname} target", datashader=datashader)
+        single_plot(fig, ax[2], lon, lat, pred, norm=norm, title=f"{vname} pred", datashader=datashader)
         single_plot(
             fig,
             ax[3],
@@ -619,7 +624,7 @@ def error_plot_in_degrees(array1: np.ndarray, array2: np.ndarray) -> np.ndarray:
                 datashader=datashader,
             )
         else:
-            single_plot(fig, ax[0], lon, lat, input_, title=f"{vname} input", datashader=datashader)
+            single_plot(fig, ax[0], lon, lat, input_, norm=norm, title=f"{vname} input", datashader=datashader)
             single_plot(
                 fig,
                 ax[4],
@@ -627,7 +632,7 @@ def error_plot_in_degrees(array1: np.ndarray, array2: np.ndarray) -> np.ndarray:
                 lat,
                 pred - input_,
                 cmap="bwr",
-                norm=TwoSlopeNorm(vcenter=0.0),
+                norm=TwoSlopeNorm(vmin=combined_error.min(), vcenter=0.0, vmax=combined_error.max()),
                 title=f"{vname} increment [pred - input]",
                 datashader=datashader,
             )
@@ -638,7 +643,7 @@ def error_plot_in_degrees(array1: np.ndarray, array2: np.ndarray) -> np.ndarray:
                 lat,
                 truth - input_,
                 cmap="bwr",
-                norm=TwoSlopeNorm(vcenter=0.0),
+                norm=TwoSlopeNorm(vmin=combined_error.min(), vcenter=0.0, vmax=combined_error.max()),
                 title=f"{vname} persist err",
                 datashader=datashader,
             )
@@ -703,13 +708,13 @@ def single_plot(
     else:
         df = pd.DataFrame({"val": data, "x": lon, "y": lat})
         # Adjust binning to match the resolution of the data
-        n_pixels = int(np.floor(data.shape[0] / 212))
+        lower_limit = 25
+        upper_limit = 500
+        n_pixels = max(min(int(np.floor(data.shape[0] * 0.004)), upper_limit), lower_limit)
         psc = dsshow(
             df,
             dsh.Point("x", "y"),
             dsh.mean("val"),
-            vmin=data.min(),
-            vmax=data.max(),
             cmap=cmap,
             plot_width=n_pixels,
             plot_height=n_pixels,
@@ -718,8 +723,10 @@ def single_plot(
             ax=ax,
         )
 
-    ax.set_xlim((-np.pi, np.pi))
-    ax.set_ylim((-np.pi / 2, np.pi / 2))
+    xmin, xmax = max(lon.min(), -np.pi), min(lon.max(), np.pi)
+    ymin, ymax = max(lat.min(), -np.pi / 2), min(lat.max(), np.pi / 2)
+    ax.set_xlim((xmin - 0.1, xmax + 0.1))
+    ax.set_ylim((ymin - 0.1, ymax + 0.1))
 
     continents.plot_continents(ax)