diff --git a/sample-data/float32_test_signal.csv b/sample-data/float32_test_signal.csv
new file mode 100644
index 00000000..e5d33fcf
--- /dev/null
+++ b/sample-data/float32_test_signal.csv
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diff --git a/tests/test_record.py b/tests/test_record.py
index fff8ef44..d447f9df 100644
--- a/tests/test_record.py
+++ b/tests/test_record.py
@@ -916,6 +916,60 @@ def test_header_with_non_utf8(self):
         ]
         assert record.units.__eq__(sig_units_target)
 
+    def test_wrsamp_float32_precision(self):
+        """
+        Test that wrsamp correctly handles float32 input without overflow.
+
+        Regression test for GitHub issue where float32 physical signals
+        caused digital overflow at boundaries due to precision loss.
+        The fix converts to float64 internally during ADC parameter
+        calculation and conversion.
+
+        Uses real-world float32 data (750 samples × 12 channels) that
+        previously triggered overflow with fmt='32'.
+        """
+        # Load the test data (float32 format)
+        test_data = np.genfromtxt(
+            "sample-data/float32_test_signal.csv", delimiter=","
+        )
+        # Ensure it's float32 (this is the key to reproducing the bug)
+        test_data = test_data.astype("float32")
+
+        # Write the record with fmt='32' (this previously caused overflow)
+        wfdb.wrsamp(
+            record_name="float32_test",
+            fs=250,
+            units=["mV"] * 12,
+            sig_name=[f"CH{i+1}" for i in range(12)],
+            p_signal=test_data,
+            fmt=["32"] * 12,
+            write_dir=self.temp_path,
+        )
+
+        # Verify we can read it back without errors
+        record = wfdb.rdrecord(
+            os.path.join(self.temp_path, "float32_test"), physical=False
+        )
+
+        # Verify no digital values are out of bounds for fmt='32'
+        dmin, dmax = -2147483648, 2147483647
+        assert np.all(record.d_signal >= dmin), "Digital values below minimum"
+        assert np.all(record.d_signal <= dmax), "Digital values above maximum"
+
+        # Verify the physical signal can be read back correctly
+        record_phys = wfdb.rdrecord(
+            os.path.join(self.temp_path, "float32_test")
+        )
+
+        # Values should be close (some precision loss is expected from ADC/DAC)
+        np.testing.assert_allclose(
+            record_phys.p_signal,
+            test_data,
+            rtol=1e-4,
+            atol=1e-6,
+            err_msg="Physical signal mismatch after round-trip",
+        )
+
     @classmethod
     def setUpClass(cls):
         cls.temp_directory = tempfile.TemporaryDirectory()
diff --git a/wfdb/io/_signal.py b/wfdb/io/_signal.py
index 6bfafdb5..c18b2606 100644
--- a/wfdb/io/_signal.py
+++ b/wfdb/io/_signal.py
@@ -538,6 +538,9 @@ def adc(self, expanded=False, inplace=False):
         # the input array is modified!
         def adc_inplace(p_signal, adc_gain, baseline, d_nan):
             nanlocs = np.isnan(p_signal)
+            # Convert to float64 for precise arithmetic to avoid precision loss
+            # that can occur with float32 input at digital value boundaries
+            p_signal = p_signal.astype("float64", copy=False)
             np.multiply(p_signal, adc_gain, p_signal)
             np.add(p_signal, baseline, p_signal)
             np.round(p_signal, 0, p_signal)
@@ -826,10 +829,14 @@ def calc_adc_params(self):
             if np.where(np.isinf(self.p_signal))[0].size:
                 raise ValueError("Signal contains inf. Cannot perform adc.")
 
+            # Convert to float64 for precise arithmetic to avoid precision loss
+            # that can occur with float32 input when computing gain and baseline
+            p_signal = self.p_signal.astype("float64", copy=False)
+
             # min and max ignoring nans, unless whole channel is NAN.
             # Should suppress warning message.
-            minvals = np.nanmin(self.p_signal, axis=0)
-            maxvals = np.nanmax(self.p_signal, axis=0)
+            minvals = np.nanmin(p_signal, axis=0)
+            maxvals = np.nanmax(p_signal, axis=0)
 
             for ch in range(np.shape(self.p_signal)[1]):
                 adc_gain, baseline = self.calc_adc_gain_baseline(
@@ -842,8 +849,10 @@ def calc_adc_params(self):
             minvals = []
             maxvals = []
             for ch in self.e_p_signal:
-                minvals.append(np.nanmin(ch))
-                maxvals.append(np.nanmax(ch))
+                # Convert to float64 for precise arithmetic
+                ch_f64 = ch.astype("float64", copy=False)
+                minvals.append(np.nanmin(ch_f64))
+                maxvals.append(np.nanmax(ch_f64))
 
             if any(x == math.inf for x in minvals) or any(
                 x == math.inf for x in maxvals