diff --git a/mne_rsa/rsa.py b/mne_rsa/rsa.py
index 6b0dbfe..98838ee 100644
--- a/mne_rsa/rsa.py
+++ b/mne_rsa/rsa.py
@@ -161,26 +161,38 @@ def rsa_gen(rdm_data_gen, rdm_model, metric="spearman", ignore_nan=False):
         masks = [~np.isnan(rdm) for rdm in rdm_model]
     else:
         masks = [slice(None)] * len(rdm_model)
+        # Precompute ranks for Spearman
+        if metric == "spearman":
+            rdm_model = [stats.rankdata(rdm) for rdm in rdm_model]
 
     for rdm_data in rdm_data_gen:
         rdm_data = _ensure_condensed(rdm_data, "rdm_data")
         if ignore_nan:
             data_mask = ~np.isnan(rdm_data)
             masks = [m & data_mask for m in masks]
-        rsa_vals = _rsa_single_rdm(rdm_data, rdm_model, metric, masks)
+        rsa_vals = _rsa_single_rdm(rdm_data, rdm_model, metric, masks, ignore_nan)
         if return_array:
             yield np.asarray(rsa_vals)
         else:
             yield rsa_vals[0]
 
 
-def _rsa_single_rdm(rdm_data, rdm_model, metric, masks):
+def _rsa_single_rdm(rdm_data, rdm_model, metric, masks, ignore_nan):
     """Compute RSA between a single data RDM and one or more model RDMs."""
     if metric == "spearman":
-        rsa_vals = [
-            stats.spearmanr(rdm_data[mask], rdm_model_[mask])[0]
-            for rdm_model_, mask in zip(rdm_model, masks)
-        ]
+        if not ignore_nan:
+            rdm_data = stats.rankdata(rdm_data)
+            rsa_vals = [
+                np.corrcoef(rdm_data, rdm_model_[mask])[0, 1]
+                for rdm_model_, mask in zip(rdm_model, masks)
+            ]
+        else:
+            rsa_vals = [
+                np.corrcoef(
+                    stats.rankdata(rdm_data[mask]), stats.rankdata(rdm_model_[mask])
+                )[0, 1]
+                for rdm_model_, mask in zip(rdm_model, masks)
+            ]
     elif metric == "pearson":
         rsa_vals = [
             stats.pearsonr(rdm_data[mask], rdm_model_[mask])[0]
@@ -423,6 +435,9 @@ def rsa_array(
         masks = [~np.isnan(rdm) for rdm in rdm_model]
     else:
         masks = [slice(None)] * len(rdm_model)
+        # Precompute ranks for Spearman
+        if rsa_metric == "spearman":
+            rdm_model = [stats.rankdata(rdm) for rdm in rdm_model]
 
     if verbose:
         from tqdm import tqdm
@@ -449,7 +464,7 @@ def rsa_single_patch(patch):
             patch_masks = [m & data_mask for m in masks]
         else:
             patch_masks = masks
-        return _rsa_single_rdm(rdm_data, rdm_model, rsa_metric, patch_masks)
+        return _rsa_single_rdm(rdm_data, rdm_model, rsa_metric, patch_masks, ignore_nan)
 
     # Call RSA multiple times in parallel for each searchlight patch
     data = Parallel(n_jobs=n_jobs)(