diff --git a/src/charts/RateDensityRidgeline.jsx b/src/charts/RateDensityRidgeline.jsx
index 54325d3..a6abe7b 100644
--- a/src/charts/RateDensityRidgeline.jsx
+++ b/src/charts/RateDensityRidgeline.jsx
@@ -2,23 +2,25 @@ import { useState } from 'react'
 import { MODEL_QUADRANTS } from '../hooks/useApi.js'
 import { raceColor, OBSERVED_MARK_COLOR, RACE_LABELS, SHORT_RACE_LABEL } from '../utils/colors.js'
 import { fitSkewedInterval, densityCurve } from '../utils/distributionApprox.js'
+import { kdeCurve } from '../utils/kde.js'
 import { niceTicks } from '../utils/niceTicks.js'
+import { useDrawDistribution } from '../hooks/useDrawDistribution.js'
 import ChartLegend from '../components/ChartLegend.jsx'
 import ApproxNote from '../components/ApproxNote.jsx'
 
 /**
  * Modeled posterior density per race×sex group, for one selected model
  * (dropdown, default three-year + referral rate), split into Female/Male
- * columns — matches whitepaper-fig-clark-density-1.png's ridge style. Plain
- * SVG — each ridge is 60 analytic points from fitSkewedInterval/densityCurve,
- * already smooth without a binning/curveBasis smoothing pass.
+ * columns. Each ridge is drawn from that group's 500 real posterior draws
+ * (Gaussian KDE) when available, falling back per-group to the analytic
+ * fitSkewedInterval/densityCurve approximation otherwise.
  */
 
 const RACE_ORDER = ['WH', 'BL', 'HI', 'AM']
 const SEX_COLUMNS = [{ sex: 'F', label: 'Female' }, { sex: 'M', label: 'Male' }]
 const DEFAULT_MODEL = 'unified_m4_mod' // Three-year + referral rate
 
-function buildGroupRow(row) {
+function buildGroupRow(row, draws) {
   const enroll = row.stu_enroll || 0
   const observedRate = enroll > 0 ? ((row.observed_arrests || 0) / enroll) * 1000 : 0
   const rateMedian = (row.rate_median || 0) * 1000
@@ -28,13 +30,16 @@ function buildGroupRow(row) {
     race: row.race,
     sex: row.sex,
     observedRate,
+    draws,
     fit: fitSkewedInterval({ median: rateMedian, lower: rateLower, upper: rateUpper }),
   }
 }
 
-export default function RateDensityRidgeline({ quadData }) {
+export default function RateDensityRidgeline({ quadData, leaid, state }) {
   const [selectedModel, setSelectedModel] = useState(DEFAULT_MODEL)
   const rows = (quadData && quadData[selectedModel]) || []
+  const groups = rows.map((r) => ({ race: r.race, sex: r.sex, stuEnroll: r.stu_enroll || 0 }))
+  const { status, drawsByGroup } = useDrawDistribution({ leaid, state, model: selectedModel, year: '21-22', groups })
 
   const modelSelect = (
     <select
@@ -55,7 +60,7 @@ export default function RateDensityRidgeline({ quadData }) {
           <h3 style={{ fontSize: '0.85rem', marginBottom: 'var(--space-1)', color: 'var(--cv-ink-2)' }}>
             Predicted arrest rates by student group
           </h3>
-          <ApproxNote />
+          {status !== 'ready' && <ApproxNote />}
         </div>
         {modelSelect}
       </div>
@@ -64,7 +69,7 @@ export default function RateDensityRidgeline({ quadData }) {
         <p style={{ color: 'var(--cv-ink-3)', marginTop: 'var(--space-2)' }}>No model data available.</p>
       ) : (
         <>
-          <RidgeColumns rows={rows} />
+          <RidgeColumns rows={rows} drawsByGroup={drawsByGroup} />
           <ChartLegend items={[
             ...RACE_ORDER.map((race) => ({ shape: 'swatch', color: raceColor(race), label: RACE_LABELS[race] })),
             { shape: 'diamond', color: OBSERVED_MARK_COLOR, label: 'Observed' },
@@ -75,7 +80,7 @@ export default function RateDensityRidgeline({ quadData }) {
   )
 }
 
-function RidgeColumns({ rows }) {
+function RidgeColumns({ rows, drawsByGroup }) {
   // Shared x-domain across both columns, so Female/Male are directly comparable.
   const allUpper = rows.map((r) => (r.rate_upper || 0) * 1000)
   const allObserved = rows
@@ -87,14 +92,24 @@ function RidgeColumns({ rows }) {
   return (
     <div style={{ display: 'grid', gridTemplateColumns: '1fr 1fr', gap: 'var(--space-3)', marginTop: 'var(--space-2)' }}>
       {SEX_COLUMNS.map(({ sex, label }) => (
-        <SexRidgeColumn key={sex} label={label} rows={rows.filter((r) => r.sex === sex)} ticks={ticks} maxRate={niceMax} />
+        <SexRidgeColumn
+          key={sex}
+          label={label}
+          rows={rows.filter((r) => r.sex === sex)}
+          drawsByGroup={drawsByGroup}
+          ticks={ticks}
+          maxRate={niceMax}
+        />
       ))}
     </div>
   )
 }
 
-function SexRidgeColumn({ label, rows, ticks, maxRate }) {
-  const groups = RACE_ORDER.map((race) => rows.find((r) => r.race === race)).filter(Boolean).map(buildGroupRow)
+function SexRidgeColumn({ label, rows, drawsByGroup, ticks, maxRate }) {
+  const groups = RACE_ORDER
+    .map((race) => rows.find((r) => r.race === race))
+    .filter(Boolean)
+    .map((row) => buildGroupRow(row, drawsByGroup?.[`${row.race}_${row.sex}`]))
 
   const width = 300
   const rowHeight = 58
@@ -104,7 +119,11 @@ function SexRidgeColumn({ label, rows, ticks, maxRate }) {
 
   const xScale = (val) => margin.left + (val / maxRate) * innerWidth
 
-  const curves = groups.map((g) => densityCurve(g.fit, { min: 0, max: maxRate, n: 60 }))
+  const curves = groups.map((g) =>
+    g.draws && g.draws.length > 0
+      ? kdeCurve(g.draws, { min: 0, max: maxRate, n: 60 })
+      : densityCurve(g.fit, { min: 0, max: maxRate, n: 60 }),
+  )
   const maxPdf = Math.max(...curves.flatMap((c) => c.map((p) => p.y)), 1e-9)
   const peakHeight = rowHeight * 0.82
 
diff --git a/src/components/ChartPanel.jsx b/src/components/ChartPanel.jsx
index 291f4c3..0b12deb 100644
--- a/src/components/ChartPanel.jsx
+++ b/src/components/ChartPanel.jsx
@@ -97,7 +97,7 @@ export default function ChartPanel({ district, state }) {
 
         <RateByGroupBar data={rateByGroup} leaid={district.leaid} state={state} />
 
-        <RateDensityRidgeline quadData={data.quadData} />
+        <RateDensityRidgeline quadData={data.quadData} leaid={district.leaid} state={state} />
       </div>
 
       {/* Methodology footer */}