diff --git a/r_app/90_CI_report_with_kpis_agronomic_support.Rmd b/r_app/90_CI_report_with_kpis_agronomic_support.Rmd
index ae1f855..e8625a8 100644
--- a/r_app/90_CI_report_with_kpis_agronomic_support.Rmd
+++ b/r_app/90_CI_report_with_kpis_agronomic_support.Rmd
@@ -1283,7 +1283,6 @@ tryCatch({
   minus_2_ww <- get_week_year(as.Date(today) - lubridate::weeks(2))
   minus_3_ww <- get_week_year(as.Date(today) - lubridate::weeks(3))
   
-  message(paste("Processing", nrow(AllPivots_merged), "fields for weeks:",
   message(paste("Processing", nrow(AllPivots_merged), "fields for weeks:",
                 current_ww$week, minus_1_ww$week, minus_2_ww$week, minus_3_ww$week))
 
diff --git a/r_app/90_rainfall_utils.R b/r_app/90_rainfall_utils.R
index ed14724..93cf099 100644
--- a/r_app/90_rainfall_utils.R
+++ b/r_app/90_rainfall_utils.R
@@ -273,6 +273,13 @@ rain_add_to_plot <- function(g, rain_joined, ci_type, x_var, y_max) {
     scale_factor <- (y_max * 0.30) / max_cum_rain
 
     g +
+      ggplot2::geom_area(
+        data        = rain_joined,
+        ggplot2::aes(x = .data[[x_var]], y = cum_rain_mm * scale_factor),
+        fill        = "steelblue",
+        alpha       = 0.15,
+        inherit.aes = FALSE
+      ) +
       ggplot2::geom_line(
         data        = rain_joined,
         ggplot2::aes(x = .data[[x_var]], y = cum_rain_mm * scale_factor),
diff --git a/r_app/90_report_utils.R b/r_app/90_report_utils.R
index 25d7d20..e81c526 100644
--- a/r_app/90_report_utils.R
+++ b/r_app/90_report_utils.R
@@ -533,6 +533,24 @@ cum_ci_plot <- function(pivotName, ci_quadrant_data = CI_quadrant, plot_type = "
                 legend.title = ggplot2::element_text(size = 8),
                 legend.text = ggplot2::element_text(size = 8)) +
           ggplot2::guides(color = ggplot2::guide_legend(nrow = 2, byrow = TRUE))
+
+        # Add benchmark lines to faceted plot (map DAH → Date per season)
+        if (!is.null(benchmark_data) && ci_type_filter %in% benchmark_data$ci_type) {
+          max_dah_clip <- max(plot_data$DAH, na.rm = TRUE) * 1.1
+          benchmark_facet <- benchmark_data %>%
+            dplyr::filter(ci_type == ci_type_filter, DAH <= max_dah_clip) %>%
+            dplyr::cross_join(
+              date_preparation_perfect_pivot %>%
+                dplyr::filter(season %in% unique_seasons) %>%
+                dplyr::select(season, min_date)
+            ) %>%
+            dplyr::mutate(Date = min_date + DAH - 1)
+          g <- g + ggplot2::geom_smooth(
+            data = benchmark_facet,
+            ggplot2::aes(x = Date, y = benchmark_value, group = factor(percentile)),
+            color = "gray70", linewidth = 0.5, se = FALSE, inherit.aes = FALSE, fullrange = FALSE
+          )
+        }
       } else {
         # Choose color palette based on colorblind_friendly flag
         color_scale <- if (colorblind_friendly) {
@@ -831,9 +849,9 @@ compute_ci_benchmarks <- function(ci_quadrant_data, estate_name, percentiles = c
     # Prepare data for both CI types
     data_prepared <- data_filtered %>%
       dplyr::ungroup() %>%  # Ensure no existing groupings
-      dplyr::select(DAH, value, cumulative_CI, season) %>%
+      dplyr::select(DAH, mean_rolling_10_days = value, cumulative_CI, season) %>%
       tidyr::pivot_longer(
-        cols = c("value", "cumulative_CI"),
+        cols = c("mean_rolling_10_days", "cumulative_CI"),
         names_to = "ci_type",
         values_to = "ci_value"
       ) %>%