diff --git a/src/ml_downscaling_emulator/score_sde_pytorch/configs/subvpsde/ukcp_local_mv_12em_cncsnpp_continuous.py b/src/ml_downscaling_emulator/score_sde_pytorch/configs/subvpsde/ukcp_local_mv_12em_cncsnpp_continuous.py
new file mode 100644
index 000000000..b6b48e0cb
--- /dev/null
+++ b/src/ml_downscaling_emulator/score_sde_pytorch/configs/subvpsde/ukcp_local_mv_12em_cncsnpp_continuous.py
@@ -0,0 +1,69 @@
+# coding=utf-8
+# Copyright 2020 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""Training NCSN++ on precip data with sub-VP SDE."""
+from ml_downscaling_emulator.score_sde_pytorch.configs.default_ukcp_local_pr_12em_configs import get_default_configs
+
+
+def get_config():
+  config = get_default_configs()
+  # training
+  training = config.training
+  training.sde = 'subvpsde'
+  training.continuous = True
+  training.reduce_mean = True
+
+  # sampling
+  sampling = config.sampling
+  sampling.method = 'pc'
+  sampling.predictor = 'euler_maruyama'
+  sampling.corrector = 'none'
+
+  # data
+  data = config.data
+  data.centered = True
+  data.dataset_name = 'bham_gcmx-4x_2em_mv'
+
+  # model
+  model = config.model
+  model.name = 'cncsnpp'
+  model.scale_by_sigma = False
+  model.ema_rate = 0.9999
+  model.normalization = 'GroupNorm'
+  model.nonlinearity = 'swish'
+  model.nf = 128
+  model.ch_mult = (1, 2, 2, 2)
+  model.num_res_blocks = 4
+  model.attn_resolutions = (16,)
+  model.resamp_with_conv = True
+  model.conditional = True
+  model.fir = True
+  model.fir_kernel = [1, 3, 3, 1]
+  model.skip_rescale = True
+  model.resblock_type = 'biggan'
+  model.progressive = 'none'
+  model.progressive_input = 'residual'
+  model.progressive_combine = 'sum'
+  model.attention_type = 'ddpm'
+  model.embedding_type = 'positional'
+  model.init_scale = 0.
+  model.fourier_scale = 16
+  model.conv_size = 3
+
+  # data
+  data = config.data
+
+  return config