diff --git a/DESCRIPTION b/DESCRIPTION
index 1b52822a..e45633b0 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: epipredict
 Title: Basic epidemiology forecasting methods
-Version: 0.1.7
+Version: 0.1.8
 Authors@R: c(
     person("Daniel J.", "McDonald", , "daniel@stat.ubc.ca", role = c("aut", "cre")),
     person("Ryan", "Tibshirani", , "ryantibs@cmu.edu", role = "aut"),
diff --git a/NEWS.md b/NEWS.md
index 2f138fd9..633086cb 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -25,6 +25,8 @@ Pre-1.0.0 numbering scheme: 0.x will indicate releases, while 0.0.x will indicat
 - Shifting no columns results in no error for either `step_epi_ahead` and `step_epi_lag`
 - Quantiles produced by `grf` were sometimes out of order.
 - dist_quantiles can have all `NA` values without causing unrelated errors
+- adjust default quantiles throughout so that they match.
+- force `layer_residual_quantiles()` to always include `0.5`.
 
 # epipredict 0.1
 
diff --git a/R/arx_forecaster.R b/R/arx_forecaster.R
index b386fe45..5397ca88 100644
--- a/R/arx_forecaster.R
+++ b/R/arx_forecaster.R
@@ -200,7 +200,8 @@ arx_fcast_epi_workflow <- function(
     } else {
       quantile_levels <- sort(compare_quantile_args(
         args_list$quantile_levels,
-        rlang::eval_tidy(trainer$eng_args$quantiles) %||% c(.1, .5, .9),
+        rlang::eval_tidy(trainer$eng_args$quantiles) %||%
+          c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
         "grf"
       ))
       trainer$eng_args$quantiles <- rlang::enquo(quantile_levels)
@@ -253,8 +254,8 @@ arx_fcast_epi_workflow <- function(
 #'   the last day of data. For example, if the last day of data was 3 days ago,
 #'   the ahead becomes `ahead+3`.
 #'   - `"extend_lags"`: increase the lags so they're relative to the actual
-#'   forecast date. For example, if the lags are `c(0,7,14)` and the last day of
-#'   data was 3 days ago, the lags become `c(3,10,17)`.
+#'   forecast date. For example, if the lags are `c(0, 7, 14)` and the last day of
+#'   data was 3 days ago, the lags become `c(3, 10, 17)`.
 #' @param warn_latency by default, `step_adjust_latency` warns the user if the
 #'   latency is large. If this is `FALSE`, that warning is turned off.
 #' @param quantile_levels Vector or `NULL`. A vector of probabilities to produce
@@ -295,7 +296,7 @@ arx_args_list <- function(
     target_date = NULL,
     adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
     warn_latency = TRUE,
-    quantile_levels = c(0.05, 0.95),
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = character(0L),
@@ -362,7 +363,7 @@ compare_quantile_args <- function(alist, tlist, train_method = c("qr", "grf")) {
   default_alist <- eval(formals(arx_args_list)$quantile_levels)
   default_tlist <- switch(train_method,
     "qr" = eval(formals(quantile_reg)$quantile_levels),
-    "grf" = c(.1, .5, .9)
+    "grf" = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)
   )
   if (setequal(alist, default_alist)) {
     if (setequal(tlist, default_tlist)) {
diff --git a/R/autoplot.R b/R/autoplot.R
index 870dcb8d..c0e3c68d 100644
--- a/R/autoplot.R
+++ b/R/autoplot.R
@@ -39,9 +39,7 @@ ggplot2::autoplot
 #'   step_epi_naomit()
 #'
 #' f <- frosting() %>%
-#'   layer_residual_quantiles(
-#'     quantile_levels = c(.025, .1, .25, .75, .9, .975)
-#'   ) %>%
+#'   layer_residual_quantiles() %>%
 #'   layer_threshold(starts_with(".pred")) %>%
 #'   layer_add_target_date()
 #'
@@ -85,7 +83,7 @@ NULL
 #' @rdname autoplot-epipred
 autoplot.epi_workflow <- function(
     object, predictions = NULL,
-    .levels = c(.5, .8, .95), ...,
+    .levels = c(.5, .8, .9), ...,
     .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
     .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
     .base_color = "dodgerblue4",
@@ -183,7 +181,7 @@ autoplot.epi_workflow <- function(
   }
 
   if (".pred" %in% names(predictions)) {
-    ntarget_dates <- n_distinct(predictions$time_value)
+    ntarget_dates <- dplyr::n_distinct(predictions$time_value)
     if (ntarget_dates > 1L) {
       bp <- bp +
         geom_line(
@@ -231,24 +229,25 @@ starts_with_impl <- function(x, vars) {
 
 plot_bands <- function(
     base_plot, predictions,
-    levels = c(.5, .8, .95),
+    levels = c(.5, .8, .9),
     fill = "blue4",
     alpha = 0.6,
     linewidth = 0.05) {
   innames <- names(predictions)
-  n <- length(levels)
-  alpha <- alpha / (n - 1)
-  l <- (1 - levels) / 2
-  l <- c(rev(l), 1 - l)
+  n_levels <- length(levels)
+  alpha <- alpha / (n_levels - 1)
+  # generate the corresponding level that is 1 - level
+  levels <- (1 - levels) / 2
+  levels <- c(rev(levels), 1 - levels)
 
   ntarget_dates <- dplyr::n_distinct(predictions$time_value)
 
   predictions <- predictions %>%
-    mutate(.pred_distn = dist_quantiles(quantile(.pred_distn, l), l)) %>%
+    mutate(.pred_distn = dist_quantiles(quantile(.pred_distn, levels), levels)) %>%
     pivot_quantiles_wider(.pred_distn)
   qnames <- setdiff(names(predictions), innames)
 
-  for (i in 1:n) {
+  for (i in 1:n_levels) {
     bottom <- qnames[i]
     top <- rev(qnames)[i]
     if (i == 1) {
diff --git a/R/extract.R b/R/extract.R
index e227b59b..2e06567e 100644
--- a/R/extract.R
+++ b/R/extract.R
@@ -13,7 +13,7 @@
 #' @examples
 #' f <- frosting() %>%
 #'   layer_predict() %>%
-#'   layer_residual_quantiles(quantile_levels = c(0.0275, 0.975), symmetrize = FALSE) %>%
+#'   layer_residual_quantiles(symmetrize = FALSE) %>%
 #'   layer_naomit(.pred)
 #'
 #' extract_argument(f, "layer_residual_quantiles", "symmetrize")
diff --git a/R/flatline_forecaster.R b/R/flatline_forecaster.R
index 7efda3ef..7faad31b 100644
--- a/R/flatline_forecaster.R
+++ b/R/flatline_forecaster.R
@@ -108,7 +108,7 @@ flatline_args_list <- function(
     n_training = Inf,
     forecast_date = NULL,
     target_date = NULL,
-    quantile_levels = c(0.05, 0.95),
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     symmetrize = TRUE,
     nonneg = TRUE,
     quantile_by_key = character(0L),
diff --git a/R/layer_quantile_distn.R b/R/layer_quantile_distn.R
index f7bc9259..b39b58f4 100644
--- a/R/layer_quantile_distn.R
+++ b/R/layer_quantile_distn.R
@@ -44,7 +44,7 @@
 #' p
 layer_quantile_distn <- function(frosting,
                                  ...,
-                                 quantile_levels = c(.25, .75),
+                                 quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
                                  truncate = c(-Inf, Inf),
                                  name = ".pred_distn",
                                  id = rand_id("quantile_distn")) {
diff --git a/R/layer_residual_quantiles.R b/R/layer_residual_quantiles.R
index e9b5b7c1..96ad8841 100644
--- a/R/layer_residual_quantiles.R
+++ b/R/layer_residual_quantiles.R
@@ -3,7 +3,8 @@
 #' @param frosting a `frosting` postprocessor
 #' @param ... Unused, include for consistency with other layers.
 #' @param quantile_levels numeric vector of probabilities with values in (0,1)
-#'   referring to the desired quantile.
+#'   referring to the desired quantile. Note that 0.5 will always be included
+#'   even if left out by the user.
 #' @param symmetrize logical. If `TRUE` then interval will be symmetric.
 #' @param by_key A character vector of keys to group the residuals by before
 #'   calculating quantiles. The default, `c()` performs no grouping.
@@ -28,7 +29,7 @@
 #' f <- frosting() %>%
 #'   layer_predict() %>%
 #'   layer_residual_quantiles(
-#'     quantile_levels = c(0.0275, 0.975),
+#'     quantile_levels = c(0.025, 0.975),
 #'     symmetrize = FALSE
 #'   ) %>%
 #'   layer_naomit(.pred)
@@ -48,7 +49,7 @@
 #' p2 <- forecast(wf2)
 layer_residual_quantiles <- function(
     frosting, ...,
-    quantile_levels = c(0.05, 0.95),
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     symmetrize = TRUE,
     by_key = character(0L),
     name = ".pred_distn",
@@ -59,6 +60,7 @@ layer_residual_quantiles <- function(
   arg_is_chr(by_key, allow_empty = TRUE)
   arg_is_probabilities(quantile_levels)
   arg_is_lgl(symmetrize)
+  quantile_levels <- sort(unique(c(0.5, quantile_levels)))
   add_layer(
     frosting,
     layer_residual_quantiles_new(
diff --git a/R/make_grf_quantiles.R b/R/make_grf_quantiles.R
index 00e7d0e7..fbd221d2 100644
--- a/R/make_grf_quantiles.R
+++ b/R/make_grf_quantiles.R
@@ -141,7 +141,7 @@ make_grf_quantiles <- function() {
       data = c(x = "X", y = "Y"),
       func = c(pkg = "grf", fun = "quantile_forest"),
       defaults = list(
-        quantiles = c(0.1, 0.5, 0.9),
+        quantiles = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
         num.threads = 1L,
         seed = rlang::expr(stats::runif(1, 0, .Machine$integer.max))
       )
diff --git a/R/make_quantile_reg.R b/R/make_quantile_reg.R
index 1388dd85..223881c8 100644
--- a/R/make_quantile_reg.R
+++ b/R/make_quantile_reg.R
@@ -12,7 +12,7 @@
 #' @param engine Character string naming the fitting function. Currently, only
 #'   "rq" and "grf" are supported.
 #' @param quantile_levels A scalar or vector of values in (0, 1) to determine which
-#'   quantiles to estimate (default is 0.5).
+#'   quantiles to estimate (default is the set 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95).
 #' @param method A fitting method used by [quantreg::rq()]. See the
 #'   documentation for a list of options.
 #'
@@ -27,7 +27,9 @@
 #' rq_spec <- quantile_reg(quantile_levels = c(.2, .8)) %>% set_engine("rq")
 #' ff <- rq_spec %>% fit(y ~ ., data = tib)
 #' predict(ff, new_data = tib)
-quantile_reg <- function(mode = "regression", engine = "rq", quantile_levels = 0.5, method = "br") {
+quantile_reg <- function(mode = "regression", engine = "rq",
+                         quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
+                         method = "br") {
   # Check for correct mode
   if (mode != "regression") {
     cli_abort("`mode` must be 'regression'")
diff --git a/R/make_smooth_quantile_reg.R b/R/make_smooth_quantile_reg.R
index 448ee0fa..f31ade3c 100644
--- a/R/make_smooth_quantile_reg.R
+++ b/R/make_smooth_quantile_reg.R
@@ -5,12 +5,7 @@
 #' the [tidymodels](https://www.tidymodels.org/) framework. Currently, the
 #' only supported engine is [smoothqr::smooth_qr()].
 #'
-#' @param mode A single character string for the type of model.
-#'   The only possible value for this model is "regression".
-#' @param engine Character string naming the fitting function. Currently, only
-#'   "smooth_qr" is supported.
-#' @param quantile_levels A scalar or vector of values in (0, 1) to determine which
-#'   quantiles to estimate (default is 0.5).
+#' @inheritParams quantile_reg
 #' @param outcome_locations Defaults to the vector `1:ncol(y)` but if the
 #'   responses are observed at a different spacing (or appear in a different
 #'   order), that information should be used here. This
@@ -76,7 +71,7 @@ smooth_quantile_reg <- function(
     mode = "regression",
     engine = "smoothqr",
     outcome_locations = NULL,
-    quantile_levels = 0.5,
+    quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
     degree = 3L) {
   # Check for correct mode
   if (mode != "regression") cli_abort("`mode` must be 'regression'")
diff --git a/man/arx_args_list.Rd b/man/arx_args_list.Rd
index f28cdefa..5ece7109 100644
--- a/man/arx_args_list.Rd
+++ b/man/arx_args_list.Rd
@@ -12,7 +12,7 @@ arx_args_list(
   target_date = NULL,
   adjust_latency = c("none", "extend_ahead", "extend_lags", "locf"),
   warn_latency = TRUE,
-  quantile_levels = c(0.05, 0.95),
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   symmetrize = TRUE,
   nonneg = TRUE,
   quantile_by_key = character(0L),
@@ -55,8 +55,8 @@ to shift the model to account for this difference. The options are:
 the last day of data. For example, if the last day of data was 3 days ago,
 the ahead becomes \code{ahead+3}.
 \item \code{"extend_lags"}: increase the lags so they're relative to the actual
-forecast date. For example, if the lags are \code{c(0,7,14)} and the last day of
-data was 3 days ago, the lags become \code{c(3,10,17)}.
+forecast date. For example, if the lags are \code{c(0, 7, 14)} and the last day of
+data was 3 days ago, the lags become \code{c(3, 10, 17)}.
 }}
 
 \item{warn_latency}{by default, \code{step_adjust_latency} warns the user if the
diff --git a/man/arx_class_args_list.Rd b/man/arx_class_args_list.Rd
index a229b67c..dbf27535 100644
--- a/man/arx_class_args_list.Rd
+++ b/man/arx_class_args_list.Rd
@@ -57,8 +57,8 @@ to shift the model to account for this difference. The options are:
 the last day of data. For example, if the last day of data was 3 days ago,
 the ahead becomes \code{ahead+3}.
 \item \code{"extend_lags"}: increase the lags so they're relative to the actual
-forecast date. For example, if the lags are \code{c(0,7,14)} and the last day of
-data was 3 days ago, the lags become \code{c(3,10,17)}.
+forecast date. For example, if the lags are \code{c(0, 7, 14)} and the last day of
+data was 3 days ago, the lags become \code{c(3, 10, 17)}.
 }}
 
 \item{warn_latency}{by default, \code{step_adjust_latency} warns the user if the
diff --git a/man/autoplot-epipred.Rd b/man/autoplot-epipred.Rd
index 1025759b..066f5538 100644
--- a/man/autoplot-epipred.Rd
+++ b/man/autoplot-epipred.Rd
@@ -9,7 +9,7 @@
 \method{autoplot}{epi_workflow}(
   object,
   predictions = NULL,
-  .levels = c(0.5, 0.8, 0.95),
+  .levels = c(0.5, 0.8, 0.9),
   ...,
   .color_by = c("all_keys", "geo_value", "other_keys", ".response", "all", "none"),
   .facet_by = c(".response", "other_keys", "all_keys", "geo_value", "all", "none"),
@@ -81,9 +81,7 @@ r <- epi_recipe(jhu) \%>\%
   step_epi_naomit()
 
 f <- frosting() \%>\%
-  layer_residual_quantiles(
-    quantile_levels = c(.025, .1, .25, .75, .9, .975)
-  ) \%>\%
+  layer_residual_quantiles() \%>\%
   layer_threshold(starts_with(".pred")) \%>\%
   layer_add_target_date()
 
diff --git a/man/extract_argument.Rd b/man/extract_argument.Rd
index 69c610c9..a276d59a 100644
--- a/man/extract_argument.Rd
+++ b/man/extract_argument.Rd
@@ -24,7 +24,7 @@ Extract an argument made to a frosting layer or recipe step
 \examples{
 f <- frosting() \%>\%
   layer_predict() \%>\%
-  layer_residual_quantiles(quantile_levels = c(0.0275, 0.975), symmetrize = FALSE) \%>\%
+  layer_residual_quantiles(symmetrize = FALSE) \%>\%
   layer_naomit(.pred)
 
 extract_argument(f, "layer_residual_quantiles", "symmetrize")
diff --git a/man/flatline_args_list.Rd b/man/flatline_args_list.Rd
index 401850ef..626bcb6f 100644
--- a/man/flatline_args_list.Rd
+++ b/man/flatline_args_list.Rd
@@ -9,7 +9,7 @@ flatline_args_list(
   n_training = Inf,
   forecast_date = NULL,
   target_date = NULL,
-  quantile_levels = c(0.05, 0.95),
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   symmetrize = TRUE,
   nonneg = TRUE,
   quantile_by_key = character(0L),
diff --git a/man/grf_quantiles.Rd b/man/grf_quantiles.Rd
index f6400edc..ad33ce11 100644
--- a/man/grf_quantiles.Rd
+++ b/man/grf_quantiles.Rd
@@ -52,8 +52,8 @@ details, see \href{https://grf-labs.github.io/grf/articles/categorical_inputs.ht
 #> Model fit template:
 #> grf::quantile_forest(X = missing_arg(), Y = missing_arg(), mtry = min_cols(~integer(1), 
 #>     x), num.trees = integer(1), min.node.size = min_rows(~integer(1), 
-#>     x), quantiles = c(0.1, 0.5, 0.9), num.threads = 1L, seed = stats::runif(1, 
-#>     0, .Machine$integer.max))
+#>     x), quantiles = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95), 
+#>     num.threads = 1L, seed = stats::runif(1, 0, .Machine$integer.max))
 }\if{html}{\out{</div>}}
 }
 
diff --git a/man/layer_quantile_distn.Rd b/man/layer_quantile_distn.Rd
index 3a5cb60e..ed4762fa 100644
--- a/man/layer_quantile_distn.Rd
+++ b/man/layer_quantile_distn.Rd
@@ -7,7 +7,7 @@
 layer_quantile_distn(
   frosting,
   ...,
-  quantile_levels = c(0.25, 0.75),
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   truncate = c(-Inf, Inf),
   name = ".pred_distn",
   id = rand_id("quantile_distn")
diff --git a/man/layer_residual_quantiles.Rd b/man/layer_residual_quantiles.Rd
index a7deded7..4efea525 100644
--- a/man/layer_residual_quantiles.Rd
+++ b/man/layer_residual_quantiles.Rd
@@ -7,7 +7,7 @@
 layer_residual_quantiles(
   frosting,
   ...,
-  quantile_levels = c(0.05, 0.95),
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   symmetrize = TRUE,
   by_key = character(0L),
   name = ".pred_distn",
@@ -20,7 +20,8 @@ layer_residual_quantiles(
 \item{...}{Unused, include for consistency with other layers.}
 
 \item{quantile_levels}{numeric vector of probabilities with values in (0,1)
-referring to the desired quantile.}
+referring to the desired quantile. Note that 0.5 will always be included
+even if left out by the user.}
 
 \item{symmetrize}{logical. If \code{TRUE} then interval will be symmetric.}
 
@@ -53,7 +54,7 @@ wf <- epi_workflow(r, linear_reg()) \%>\% fit(jhu)
 f <- frosting() \%>\%
   layer_predict() \%>\%
   layer_residual_quantiles(
-    quantile_levels = c(0.0275, 0.975),
+    quantile_levels = c(0.025, 0.975),
     symmetrize = FALSE
   ) \%>\%
   layer_naomit(.pred)
diff --git a/man/quantile_reg.Rd b/man/quantile_reg.Rd
index 5079c343..31a5dd12 100644
--- a/man/quantile_reg.Rd
+++ b/man/quantile_reg.Rd
@@ -7,7 +7,7 @@
 quantile_reg(
   mode = "regression",
   engine = "rq",
-  quantile_levels = 0.5,
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   method = "br"
 )
 }
@@ -19,7 +19,7 @@ The only possible value for this model is "regression".}
 "rq" and "grf" are supported.}
 
 \item{quantile_levels}{A scalar or vector of values in (0, 1) to determine which
-quantiles to estimate (default is 0.5).}
+quantiles to estimate (default is the set 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95).}
 
 \item{method}{A fitting method used by \code{\link[quantreg:rq]{quantreg::rq()}}. See the
 documentation for a list of options.}
diff --git a/man/smooth_quantile_reg.Rd b/man/smooth_quantile_reg.Rd
index c6b17dd8..90b2c104 100644
--- a/man/smooth_quantile_reg.Rd
+++ b/man/smooth_quantile_reg.Rd
@@ -8,7 +8,7 @@ smooth_quantile_reg(
   mode = "regression",
   engine = "smoothqr",
   outcome_locations = NULL,
-  quantile_levels = 0.5,
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   degree = 3L
 )
 }
@@ -17,7 +17,7 @@ smooth_quantile_reg(
 The only possible value for this model is "regression".}
 
 \item{engine}{Character string naming the fitting function. Currently, only
-"smooth_qr" is supported.}
+"rq" and "grf" are supported.}
 
 \item{outcome_locations}{Defaults to the vector \code{1:ncol(y)} but if the
 responses are observed at a different spacing (or appear in a different
@@ -25,7 +25,7 @@ order), that information should be used here. This
 argument will be mapped to the \code{ahead} argument of \code{\link[smoothqr:smooth_qr]{smoothqr::smooth_qr()}}.}
 
 \item{quantile_levels}{A scalar or vector of values in (0, 1) to determine which
-quantiles to estimate (default is 0.5).}
+quantiles to estimate (default is the set 0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95).}
 
 \item{degree}{the number of polynomials used for response smoothing. Must
 be no more than the number of responses.}
diff --git a/tests/testthat/_snaps/snapshots.md b/tests/testthat/_snaps/snapshots.md
index 9fd339de..17191e04 100644
--- a/tests/testthat/_snaps/snapshots.md
+++ b/tests/testthat/_snaps/snapshots.md
@@ -3,24 +3,32 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.34820911), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
+    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
+    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.31206391), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.10325949, 0.52098931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
+        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.63071101), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.52311949, 0.94084931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
+        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.40640751), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", "list"
-    )), forecast_date = structure(c(18992, 18992, 18992, 18992, 18992, 
-    18992), class = "Date"), target_date = structure(c(18999, 18999, 
-    18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
+        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
+        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
+        0.32698722, 0.40640751), quantile_levels = c(0.05, 0.1, 0.25, 
+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
+    18992, 18992), class = "Date"), target_date = structure(c(18999, 
+    18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
@@ -28,24 +36,31 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0.084583345, 
-    0.194105055), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    0.1073314, 0.1292864, 0.1393442, 0.149402, 0.171357, 0.194105055
+    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.048438145, 0.157959855), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.257363545, 0.366885255
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.048438145, 0.0711862, 0.0931412, 0.103199, 0.1132568, 
+        0.1352118, 0.157959855), quantile_levels = c(0.05, 0.1, 0.25, 
+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.257363545, 
+    0.2801116, 0.3020666, 0.3121244, 0.3221822, 0.3441372, 0.366885255
+    ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.367085245, 0.476606955), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.677223545, 0.786745255
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.367085245, 0.3898333, 0.4117883, 0.4218461, 
+        0.4319039, 0.4538589, 0.476606955), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.142781745, 0.252303455), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", "list"
-    )), forecast_date = structure(c(18992, 18992, 18992, 18992, 18992, 
-    18992), class = "Date"), target_date = structure(c(18993, 18993, 
-    18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
+        values = c(0.677223545, 0.6999716, 0.7219266, 0.7319844, 
+        0.7420422, 0.7639972, 0.786745255), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(0.142781745, 0.1655298, 0.1874848, 0.1975426, 
+        0.2076004, 0.2295554, 0.252303455), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+    "vctrs_vctr", "list")), forecast_date = structure(c(18992, 18992, 
+    18992, 18992, 18992, 18992), class = "Date"), target_date = structure(c(18993, 
+    18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
@@ -53,24 +68,32 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.34820911), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
+    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
+    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
+        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.31206391), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.10325949, 0.52098931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
+        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.63071101), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.52311949, 0.94084931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
+        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.40640751), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", "list"
-    )), forecast_date = structure(c(18992, 18992, 18992, 18992, 18992, 
-    18992), class = "Date"), target_date = structure(c(18999, 18999, 
-    18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
+        values = c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
+        0.32698722, 0.40640751), quantile_levels = c(0.05, 0.1, 0.25, 
+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
+    18992, 18992), class = "Date"), target_date = structure(c(18999, 
+    18999, 18999, 18999, 18999, 18999), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
@@ -78,24 +101,32 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.1393442, 0.103199, 0.3121244, 0.4218461, 0.7319844, 
     0.1975426), .pred_distn = structure(list(structure(list(values = c(0, 
-    0.34820911), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    0.00989957999999999, 0.09353595, 0.1393442, 0.18515245, 0.26878882, 
+    0.34820911), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 
+    0.9, 0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+    "vctrs_vctr")), structure(list(values = c(0, 0, 0.05739075, 0.103199, 
+    0.14900725, 0.23264362, 0.31206391), quantile_levels = c(0.05, 
+    0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
+    "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
+        values = c(0.10325949, 0.18267978, 0.26631615, 0.3121244, 
+        0.35793265, 0.44156902, 0.52098931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.31206391), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.10325949, 0.52098931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.21298119, 0.29240148, 0.37603785, 0.4218461, 
+        0.46765435, 0.55129072, 0.63071101), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0.21298119, 0.63071101), quantile_levels = c(0.05, 
-        0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr")), structure(list(values = c(0.52311949, 0.94084931
-    ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        values = c(0.52311949, 0.60253978, 0.68617615, 0.7319844, 
+        0.77779265, 0.86142902, 0.94084931), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
     "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-        values = c(0, 0.40640751), quantile_levels = c(0.05, 0.95
-        )), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-    "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", "list"
-    )), forecast_date = structure(c(18992, 18992, 18992, 18992, 18992, 
-    18992), class = "Date"), target_date = structure(c(18993, 18993, 
-    18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
+        values = c(0, 0.06809798, 0.15173435, 0.1975426, 0.24335085, 
+        0.32698722, 0.40640751), quantile_levels = c(0.05, 0.1, 0.25, 
+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+    "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
+    "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
+    18992, 18992), class = "Date"), target_date = structure(c(18993, 
+    18993, 18993, 18993, 18993, 18993), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 # cdc_baseline_forecaster snapshots
@@ -983,20 +1014,33 @@
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
     ), .pred = c(0.353013358779435, 0.648525432444877, 0.667670289394328, 
     1.1418673907239, 0.830448695683587, 0.329799431948649), .pred_distn = structure(list(
-        structure(list(values = c(0.171022956902288, 0.535003760656582
-        ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.46653503056773, 0.830515834322024), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.485679887517181, 
-        0.849660691271475), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        structure(list(values = c(0.171022956902288, 0.244945899624723, 
+        0.308032696431071, 0.353013358779435, 0.397994021127798, 
+        0.461080817934147, 0.535003760656582), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.959876988846753, 1.32385779260105), quantile_levels = c(0.05, 
+            values = c(0.46653503056773, 0.540457973290166, 0.603544770096514, 
+            0.648525432444877, 0.693506094793241, 0.756592891599589, 
+            0.830515834322024), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.485679887517181, 
+        0.559602830239616, 0.622689627045964, 0.667670289394328, 
+        0.712650951742692, 0.77573774854904, 0.849660691271475), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.64845829380644, 
-        1.01243909756073), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        "vctrs_vctr")), structure(list(values = c(0.959876988846753, 
+        1.03379993156919, 1.09688672837554, 1.1418673907239, 1.18684805307226, 
+        1.24993484987861, 1.32385779260105), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.147809030071502, 0.511789833825796), quantile_levels = c(0.05, 
+            values = c(0.64845829380644, 0.722381236528875, 0.785468033335223, 
+            0.830448695683587, 0.875429358031951, 0.938516154838299, 
+            1.01243909756073), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.147809030071502, 
+        0.221731972793937, 0.284818769600285, 0.329799431948649, 
+        0.374780094297013, 0.437866891103361, 0.511789833825796), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
@@ -1007,22 +1051,35 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.149303403634372, 0.139764664505947, 0.333186321066645, 
-    0.470345577837143, 0.725986105412007, 0.212686665274007), .pred_distn = structure(list(
-        structure(list(values = c(0.0961118191398633, 0.202494988128882
-        ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+    ), .pred = c(0.149303403634373, 0.139764664505948, 0.333186321066645, 
+    0.470345577837144, 0.725986105412008, 0.212686665274007), .pred_distn = structure(list(
+        structure(list(values = c(0.0961118191398634, 0.118312393281548, 
+        0.13840396557592, 0.149303403634373, 0.160202841692825, 0.180294413987198, 
+        0.202494988128882), quantile_levels = c(0.05, 0.1, 0.25, 
+        0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.0865730800114383, 
+        0.108773654153123, 0.128865226447495, 0.139764664505948, 
+        0.1506641025644, 0.170755674858773, 0.192956249000457), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.0865730800114382, 0.192956249000457), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.279994736572135, 
-        0.386377905561154), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.417153993342634, 0.523537162331653), quantile_levels = c(0.05, 
+            values = c(0.279994736572136, 0.30219531071382, 0.322286883008193, 
+            0.333186321066645, 0.344085759125097, 0.36417733141947, 
+            0.386377905561154), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.417153993342634, 
+        0.439354567484319, 0.459446139778691, 0.470345577837144, 
+        0.481245015895596, 0.501336588189969, 0.523537162331653), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr")), structure(list(values = c(0.672794520917498, 
-        0.779177689906517), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
-        "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.159495080779498, 0.265878249768516), quantile_levels = c(0.05, 
+        0.694995095059183, 0.715086667353556, 0.725986105412008, 
+        0.73688554347046, 0.756977115764833, 0.779177689906517), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
+            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
+        "vctrs_vctr")), structure(list(values = c(0.159495080779498, 
+        0.181695654921182, 0.201787227215555, 0.212686665274007, 
+        0.223586103332459, 0.243677675626832, 0.265878249768516), 
+            quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
     "list")), forecast_date = structure(c(18992, 18992, 18992, 18992, 
@@ -1033,52 +1090,76 @@
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
-        structure(list(values = c(0.136509784083987, 0.469979623951498
-        ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        structure(list(values = c(0.136509784083987, 0.202348949370703, 
+        0.263837900408968, 0.303244704017742, 0.342651507626517, 
+        0.404140458664782, 0.469979623951498), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.364597933377326, 0.698067773244837), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
-        0.755562864619735), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.364597933377326, 0.430437098664042, 0.491926049702307, 
+            0.531332853311081, 0.570739656919856, 0.632228607958121, 
+            0.698067773244837), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
+        0.48793219003894, 0.549421141077205, 0.58882794468598, 0.628234748294754, 
+        0.689723699333019, 0.755562864619735), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282475, 1.15542516914999), quantile_levels = c(0.05, 
+            values = c(0.821955329282475, 0.887794494569191, 0.949283445607456, 
+            0.98869024921623, 1.028097052825, 1.08958600386327, 1.15542516914999
+            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
-        0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        0.693906242354601, 0.755395193392866, 0.79480199700164, 0.834208800610414, 
+        0.895697751648679, 0.961536916935395), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
-    18997, 18997), class = "Date"), target_date = structure(c(18998, 
+            values = c(0.140160537291566, 0.205999702578282, 0.267488653616547, 
+            0.306895457225321, 0.346302260834096, 0.407791211872361, 
+            0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+    "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
+    18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
 ---
 
     structure(list(geo_value = c("ca", "fl", "ga", "ny", "pa", "tx"
-    ), .pred = c(0.303244704017742, 0.531332853311081, 0.588827944685979, 
+    ), .pred = c(0.303244704017742, 0.531332853311081, 0.58882794468598, 
     0.98869024921623, 0.79480199700164, 0.306895457225321), .pred_distn = structure(list(
-        structure(list(values = c(0.136509784083987, 0.469979623951498
-        ), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        structure(list(values = c(0.136509784083987, 0.202348949370703, 
+        0.263837900408968, 0.303244704017742, 0.342651507626517, 
+        0.404140458664782, 0.469979623951498), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.364597933377326, 0.698067773244837), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
-        0.755562864619735), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+            values = c(0.364597933377326, 0.430437098664042, 0.491926049702307, 
+            0.531332853311081, 0.570739656919856, 0.632228607958121, 
+            0.698067773244837), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr")), structure(list(values = c(0.422093024752224, 
+        0.48793219003894, 0.549421141077205, 0.58882794468598, 0.628234748294754, 
+        0.689723699333019, 0.755562864619735), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.821955329282475, 1.15542516914999), quantile_levels = c(0.05, 
+            values = c(0.821955329282475, 0.887794494569191, 0.949283445607456, 
+            0.98869024921623, 1.028097052825, 1.08958600386327, 1.15542516914999
+            ), quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 
             0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
         "vctrs_vctr")), structure(list(values = c(0.628067077067884, 
-        0.961536916935395), quantile_levels = c(0.05, 0.95)), class = c("dist_quantiles", 
+        0.693906242354601, 0.755395193392866, 0.79480199700164, 0.834208800610414, 
+        0.895697751648679, 0.961536916935395), quantile_levels = c(0.05, 
+        0.1, 0.25, 0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", 
         "dist_default", "vctrs_rcrd", "vctrs_vctr")), structure(list(
-            values = c(0.140160537291565, 0.473630377159077), quantile_levels = c(0.05, 
-            0.95)), class = c("dist_quantiles", "dist_default", "vctrs_rcrd", 
-        "vctrs_vctr"))), class = c("distribution", "vctrs_vctr", 
-    "list")), forecast_date = structure(c(18997, 18997, 18997, 18997, 
-    18997, 18997), class = "Date"), target_date = structure(c(18998, 
+            values = c(0.140160537291566, 0.205999702578282, 0.267488653616547, 
+            0.306895457225321, 0.346302260834096, 0.407791211872361, 
+            0.473630377159077), quantile_levels = c(0.05, 0.1, 0.25, 
+            0.5, 0.75, 0.9, 0.95)), class = c("dist_quantiles", "dist_default", 
+        "vctrs_rcrd", "vctrs_vctr"))), class = c("distribution", 
+    "vctrs_vctr", "list")), forecast_date = structure(c(18997, 18997, 
+    18997, 18997, 18997, 18997), class = "Date"), target_date = structure(c(18998, 
     18998, 18998, 18998, 18998, 18998), class = "Date")), row.names = c(NA, 
     -6L), class = c("tbl_df", "tbl", "data.frame"))
 
diff --git a/tests/testthat/test-arx_args_list.R b/tests/testthat/test-arx_args_list.R
index 226444e3..50379357 100644
--- a/tests/testthat/test-arx_args_list.R
+++ b/tests/testthat/test-arx_args_list.R
@@ -41,7 +41,7 @@ test_that("arx forecaster disambiguates quantiles", {
   tlist <- eval(formals(quantile_reg)$quantile_levels)
   expect_identical( # both default
     compare_quantile_args(alist, tlist),
-    sort(c(alist, tlist))
+    c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)
   )
   expect_snapshot(
     error = TRUE,
diff --git a/tests/testthat/test-extract_argument.R b/tests/testthat/test-extract_argument.R
index 7ac160e6..e6063228 100644
--- a/tests/testthat/test-extract_argument.R
+++ b/tests/testthat/test-extract_argument.R
@@ -8,20 +8,20 @@ test_that("layer argument extractor works", {
   expect_snapshot(error = TRUE, extract_argument(f$layers[[1]], "layer_predict", "bubble"))
   expect_identical(
     extract_argument(f$layers[[2]], "layer_residual_quantiles", "quantile_levels"),
-    c(0.0275, 0.9750)
+    c(0.0275, 0.5, 0.9750)
   )
 
   expect_snapshot(error = TRUE, extract_argument(f, "layer_thresh", "quantile_levels"))
   expect_identical(
     extract_argument(f, "layer_residual_quantiles", "quantile_levels"),
-    c(0.0275, 0.9750)
+    c(0.0275, 0.5, 0.9750)
   )
 
   wf <- epi_workflow(postprocessor = f)
   expect_snapshot(error = TRUE, extract_argument(epi_workflow(), "layer_residual_quantiles", "quantile_levels"))
   expect_identical(
     extract_argument(wf, "layer_residual_quantiles", "quantile_levels"),
-    c(0.0275, 0.9750)
+    c(0.0275, 0.5, 0.9750)
   )
 
   expect_snapshot(error = TRUE, extract_argument(wf, "layer_predict", c("type", "opts")))
diff --git a/tests/testthat/test-grf_quantiles.R b/tests/testthat/test-grf_quantiles.R
index e2cf90cf..5adbf651 100644
--- a/tests/testthat/test-grf_quantiles.R
+++ b/tests/testthat/test-grf_quantiles.R
@@ -9,7 +9,10 @@ test_that("quantile_rand_forest defaults work", {
   spec <- rand_forest(engine = "grf_quantiles", mode = "regression")
   expect_silent(out <- fit(spec, formula = y ~ x + z, data = tib))
   pars <- parsnip::extract_fit_engine(out)
-  manual <- quantile_forest(as.matrix(tib[, 2:3]), tib$y, quantiles = c(0.1, 0.5, 0.9))
+  manual <- quantile_forest(
+    as.matrix(tib[, 2:3]), tib$y,
+    quantiles = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95)
+  )
   expect_identical(pars$quantiles.orig, manual$quantiles.orig)
   expect_identical(pars$`_num_trees`, manual$`_num_trees`)
 
@@ -43,7 +46,8 @@ test_that("quantile_rand_forest handles alternative quantiles", {
 
 
 test_that("quantile_rand_forest handles allows setting the trees and mtry", {
-  spec <- rand_forest(mode = "regression", mtry = 2, trees = 100, engine = "grf_quantiles")
+  spec <- rand_forest(mode = "regression", mtry = 2, trees = 100) %>%
+    set_engine(engine = "grf_quantiles", quantiles = c(0.1, 0.5, 0.9))
   expect_silent(out <- fit(spec, formula = y ~ x + z, data = tib))
   pars <- parsnip::extract_fit_engine(out)
   manual <- quantile_forest(as.matrix(tib[, 2:3]), tib$y, mtry = 2, num.trees = 100)
@@ -68,13 +72,13 @@ test_that("quantile_rand_forest operates with arx_forecaster", {
   spec2 <- parsnip::extract_spec_parsnip(o)
   expect_identical(
     rlang::eval_tidy(spec2$eng_args$quantiles),
-    c(.05, .1, .5, .9, .95) # merged with arx_args default
+    c(.05, .1, 0.25, .5, 0.75, .9, .95) # merged with arx_args default
   )
   df <- epidatasets::counts_subset %>% filter(time_value >= "2021-10-01")
 
   z <- arx_forecaster(df, "cases", "cases", spec2)
   expect_identical(
     nested_quantiles(z$predictions$.pred_distn[1])[[1]]$quantile_levels,
-    c(.05, .1, .5, .9, .95)
+    c(.05, .1, 0.25, .5, 0.75, .9, .95)
   )
 })
diff --git a/tests/testthat/test-layer_residual_quantiles.R b/tests/testthat/test-layer_residual_quantiles.R
index 12e44809..1736ded2 100644
--- a/tests/testthat/test-layer_residual_quantiles.R
+++ b/tests/testthat/test-layer_residual_quantiles.R
@@ -26,8 +26,8 @@ test_that("Returns expected number or rows and columns", {
   nested <- p %>% dplyr::mutate(.quantiles = nested_quantiles(.pred_distn))
   unnested <- nested %>% tidyr::unnest(.quantiles)
 
-  expect_equal(nrow(unnested), 9L)
-  expect_equal(unique(unnested$quantile_levels), c(.0275, .8, .95))
+  expect_equal(nrow(unnested), 12L)
+  expect_equal(unique(unnested$quantile_levels), c(.0275, .5, .8, .95))
 })
 
 
diff --git a/tests/testthat/test-layer_threshold_preds.R b/tests/testthat/test-layer_threshold_preds.R
index f3e90c21..2ff43a16 100644
--- a/tests/testthat/test-layer_threshold_preds.R
+++ b/tests/testthat/test-layer_threshold_preds.R
@@ -58,6 +58,9 @@ test_that("thresholds additional columns", {
   p <- p %>%
     dplyr::mutate(.quantiles = nested_quantiles(.pred_distn)) %>%
     tidyr::unnest(.quantiles)
-  expect_equal(round(p$values, digits = 3), c(0.180, 0.31, 0.180, .18, 0.310, .31))
-  expect_equal(p$quantile_levels, rep(c(.1, .9), times = 3))
+  expect_equal(
+    round(p$values, digits = 3),
+    c(0.180, 0.180, 0.31, 0.180, 0.180, .18, 0.310, .31, .31)
+  )
+  expect_equal(p$quantile_levels, rep(c(.1, 0.5, .9), times = 3))
 })
diff --git a/vignettes/epipredict.Rmd b/vignettes/epipredict.Rmd
index 2cf7037c..ce0a7e38 100644
--- a/vignettes/epipredict.Rmd
+++ b/vignettes/epipredict.Rmd
@@ -208,7 +208,7 @@ quantiles.
 ```{r differential-levels}
 out_q <- arx_forecaster(jhu, "death_rate", c("case_rate", "death_rate"),
   args_list = arx_args_list(
-    quantile_levels = c(.01, .025, seq(.05, .95, by = .05), .975, .99)
+    quantile_levels = c(.01, .025, 1:19 / 20, .975, .99)
   )
 )
 ```
@@ -237,7 +237,8 @@ function:
 ```{r, eval = FALSE}
 arx_args_list(
   lags = c(0L, 7L, 14L), ahead = 7L, n_training = Inf,
-  forecast_date = NULL, target_date = NULL, quantile_levels = c(0.05, 0.95),
+  forecast_date = NULL, target_date = NULL,
+  quantile_levels = c(0.05, 0.1, 0.25, 0.5, 0.75, 0.9, 0.95),
   symmetrize = TRUE, nonneg = TRUE, quantile_by_key = character(0L),
   nafill_buffer = Inf
 )
@@ -407,7 +408,7 @@ intervals at 0. The code to do this (inside the forecaster) is
 f <- frosting() %>%
   layer_predict() %>%
   layer_residual_quantiles(
-    quantile_levels = c(.01, .025, seq(.05, .95, by = .05), .975, .99),
+    quantile_levels = c(.01, .025, 1:19 / 20, .975, .99),
     symmetrize = TRUE
   ) %>%
   layer_add_forecast_date() %>%
diff --git a/vignettes/panel-data.Rmd b/vignettes/panel-data.Rmd
index 1faf5b56..e9905789 100644
--- a/vignettes/panel-data.Rmd
+++ b/vignettes/panel-data.Rmd
@@ -364,7 +364,6 @@ f <- frosting() %>%
   layer_threshold(.pred, lower = 0) %>%
   # 90% prediction interval
   layer_residual_quantiles(
-    quantile_levels = c(0.1, 0.9),
     symmetrize = FALSE
   ) %>%
   layer_population_scaling(
diff --git a/vignettes/preprocessing-and-models.Rmd b/vignettes/preprocessing-and-models.Rmd
index 8d1d2f19..6bff4561 100644
--- a/vignettes/preprocessing-and-models.Rmd
+++ b/vignettes/preprocessing-and-models.Rmd
@@ -381,7 +381,7 @@ f <- frosting() %>%
     df_pop_col = "pop"
   )
 
-wf <- epi_workflow(r, quantile_reg(quantile_levels = c(.05, .5, .95))) %>%
+wf <- epi_workflow(r, quantile_reg()) %>%
   fit(jhu) %>%
   add_frosting(f)