diff --git a/openhexa/toolbox/era5/aggregate.py b/openhexa/toolbox/era5/aggregate.py
index 888f9c2..0f683ca 100644
--- a/openhexa/toolbox/era5/aggregate.py
+++ b/openhexa/toolbox/era5/aggregate.py
@@ -238,7 +238,9 @@ def aggregate(ds: xr.Dataset, var: str, masks: np.ndarray, boundaries_id: list[s
     return df
 
 
-def aggregate_per_week(daily: pl.DataFrame, column_uid: str, use_epidemiological_weeks: bool = False) -> pl.DataFrame:
+def aggregate_per_week(
+    daily: pl.DataFrame, column_uid: str, use_epidemiological_weeks: bool = False, sum_aggregation: bool = False
+) -> pl.DataFrame:
     """Aggregate daily data per week.
 
     Parameters
@@ -248,6 +250,10 @@ def aggregate_per_week(daily: pl.DataFrame, column_uid: str, use_epidemiological
         Length of the dataframe should be (n_boundaries * n_days).
     column_uid : str
         Column containing the boundary ID.
+    use_epidemiological_weeks : bool, optional
+        Use epidemiological weeks instead of iso weeks.
+    sum_aggregation : bool, optional
+        If True, sum values instead of computing the mean, for example for total precipitation data.
 
     Returns
     -------
@@ -261,15 +267,22 @@ def aggregate_per_week(daily: pl.DataFrame, column_uid: str, use_epidemiological
 
     df = daily.select([column_uid, pl.col(week_column).alias("week"), "mean", "min", "max"])
 
-    # by default, we aggregate min & max values by respectively using the min() and max()
-    # functions, however it might makes sense to use mean() for some use cases
-    df = df.group_by([column_uid, "week"]).agg(
-        [
-            pl.col("mean").mean().alias("mean"),
-            pl.col("min").min().alias("min"),
-            pl.col("max").max().alias("max"),
-        ]
-    )
+    if sum_aggregation:
+        df = df.group_by([column_uid, "week"]).agg(
+            [
+                pl.col("mean").sum().alias("mean"),
+                pl.col("min").sum().alias("min"),
+                pl.col("max").sum().alias("max"),
+            ]
+        )
+    else:
+        df = df.group_by([column_uid, "week"]).agg(
+            [
+                pl.col("mean").mean().alias("mean"),
+                pl.col("min").min().alias("min"),
+                pl.col("max").max().alias("max"),
+            ]
+        )
 
     # sort per date since dhis2 period format is "2012W9", we need to extract year and week number
     # from the period string and cast them to int before sorting, else "2012W9" will be superior to
@@ -285,7 +298,7 @@ def aggregate_per_week(daily: pl.DataFrame, column_uid: str, use_epidemiological
     return df
 
 
-def aggregate_per_month(daily: pl.DataFrame, column_uid: str) -> pl.DataFrame:
+def aggregate_per_month(daily: pl.DataFrame, column_uid: str, sum_aggregation: bool = False) -> pl.DataFrame:
     """Aggregate daily data per month.
 
     Parameters
@@ -295,6 +308,8 @@ def aggregate_per_month(daily: pl.DataFrame, column_uid: str) -> pl.DataFrame:
         Length of the dataframe should be (n_boundaries * n_days).
     column_uid : str
         Column containing the boundary ID.
+    sum_aggregation : bool, optional
+        If True, sum values instead of computing the mean, for example for total precipitation data.
 
     Returns
     -------
@@ -303,13 +318,22 @@ def aggregate_per_month(daily: pl.DataFrame, column_uid: str) -> pl.DataFrame:
     """
     df = daily.select([column_uid, "month", "mean", "min", "max"])
 
-    df = df.group_by([column_uid, "month"]).agg(
-        [
-            pl.col("mean").mean().alias("mean"),
-            pl.col("min").min().alias("min"),
-            pl.col("max").max().alias("max"),
-        ]
-    )
+    if sum_aggregation:
+        df = df.group_by([column_uid, "month"]).agg(
+            [
+                pl.col("mean").sum().alias("mean"),
+                pl.col("min").sum().alias("min"),
+                pl.col("max").sum().alias("max"),
+            ]
+        )
+    else:
+        df = df.group_by([column_uid, "month"]).agg(
+            [
+                pl.col("mean").mean().alias("mean"),
+                pl.col("min").min().alias("min"),
+                pl.col("max").max().alias("max"),
+            ]
+        )
 
     df = df.sort(
         by=[