diff --git a/src/ml_downscaling_emulator/data.py b/src/ml_downscaling_emulator/data.py
index b388539a3..162d3ae7f 100644
--- a/src/ml_downscaling_emulator/data.py
+++ b/src/ml_downscaling_emulator/data.py
@@ -180,11 +180,12 @@ def np_samples_to_xr(np_samples, target_transform, target_vars, coords, cf_data_
         pred_var = (pred_dims, np_var_pred, pred_attrs)
         raw_pred_var = (
             pred_dims,
+            np_var_pred,
             {"grid_mapping": "rotated_latitude_longitude"},
         )
         data_vars.update(
             {
-                var.replace("target_", "pred_"): pred_var,
+                var: pred_var,  # don't rename pred var until after inverting target transform
                 var.replace("target_", "raw_pred_"): raw_pred_var,
             }
         )
diff --git a/tests/smoke-test b/tests/smoke-test
index 21ff64615..91dee90b2 100755
--- a/tests/smoke-test
+++ b/tests/smoke-test
@@ -2,25 +2,23 @@
 
 set -euo pipefail
 
-config_name="ukcp_local_pr_1em_cunet_continuous"
-dataset="bham_gcmx-4x_1em_psl-sphum4th-temp4th-vort4th_eqvt_random-season-historic"
-
 sde="subvpsde"
+config_name="ukcp_local_mv_12em_cncsnpp_continuous"
+dataset="bham_gcmx-4x_2em_mv"
+
 workdir="output/test/${sde}/${config_name}/test-run"
 config_path="src/ml_downscaling_emulator/score_sde_pytorch/configs/${sde}/${config_name}.py"
 
 loc_spec_channels=0
 train_batch_size=2
-random_crop_size=32
 epoch=2
 
 rm -rf ${workdir}
-WANDB_EXPERIMENT_NAME="test" python bin/main.py --workdir ${workdir} --config ${config_path} --mode train --config.data.dataset_name=${dataset} --config.training.snapshot_freq=5 --config.training.eval_freq=100 --config.training.log_freq=50 --config.training.n_epochs=${epoch} --config.model.loc_spec_channels=${loc_spec_channels} --config.training.random_crop_size=${random_crop_size} --config.training.batch_size=${train_batch_size} --config.data.input_transform_key=stan --config.data.target_transform_key=sqrturrecen --config.data.time_inputs=True
-
+WANDB_EXPERIMENT_NAME="test" python bin/main.py --workdir ${workdir} --config ${config_path} --mode train --config.data.dataset_name=${dataset} --config.training.snapshot_freq=5 --config.training.eval_freq=100 --config.training.log_freq=50 --config.training.n_epochs=${epoch} --config.model.loc_spec_channels=${loc_spec_channels} --config.training.batch_size=${train_batch_size} --config.data.input_transform_key=stan --config.data.target_transform_key=sqrturrecen --config.data.time_inputs=True --config.model.name=cunet --config.model.num_scales=10
 
 num_samples=2
-eval_batchsize=32
+eval_batch_size=32
 checkpoint="epoch_${epoch}"
 
 rm -rf "${workdir}/samples/${checkpoint}/${dataset}"
-python bin/predict.py ${workdir} --dataset ${dataset} --checkpoint ${checkpoint} --batch-size ${eval_batchsize} --num-samples ${num_samples} --ensemble-member 01
+python bin/predict.py ${workdir} --dataset ${dataset} --checkpoint ${checkpoint} --batch-size ${eval_batch_size} --num-samples ${num_samples} --ensemble-member 01