Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa

David Sagna; Redouane Lguensat; Samo Diatta

physics.ao-phOct 12, 2026

Statistical Downscaling of Daily Precipitation from ERA5 Atmospheric Fields to IMERG over West Africa

Authors: David Sagna, Redouane Lguensat, Samo Diatta

Organizations: Laboratoire d’Océanographie et des Sciences de l’Environnement et du Climat (LOSEC), Université Assane Seck de Ziguinchor, BP 523, Ziguinchor, Senegal · Institut Pierre-Simon Laplace (IPSL), Sorbonne Université, IRD, 75005 Paris, France

Abstract

Global reanalyses are too coarse to resolve the convective systems that produce most West African monsoon rainfall. In this work, we use U-Net based architectures to downscale daily ERA5 fields (0.25°) to IMERG V07 satellite precipitation (0.1°) over West Africa for the June to October seasons of 2000 to 2024, using geopotential, temperature, humidity and wind at three pressure levels together with elevation and land fraction. Adding ERA5 precipitation as input slightly improves performance for this particular challenging region. We find that all the deterministic networks investigated for this work are too dry and lose almost all observed variance below a few hundred kilometers, which motivated our use of a GAN-based alternative. We also investigated permutation importance for the U-Net that ranks 500 hPa specific humidity first, consistent with the sensitivity of deep convection to mid-tropospheric moisture.

Figures & tables

Figure 1: Study domain and mean IMERG V07 precipitation over the June–October seasons of 2000–2024 (mm d -1 ). The black line is the coastline (land fraction 0.5), the brown contours mark elevations of 500 and 1000 m, and the dashed lines at 10°N and 15°N separate the Guinean, Sudanian and Sahelian zones.

Raw outputs
	RMSE	MAE	Bias	$r$	P99	$\mathrm{FSS}_{20}$	$\mathrm{CSI}_{5}$	$\mathrm{CSI}_{20}$	$\mathrm{CSI}_{40}$
					ratio
Climatology	9.94	4.82	$-$ 0.13	0.386	0.34	0.064	0.334	0.025	0.000
ERA5 precipitation	10.71	4.17	$-$ 0.38	0.465	0.85	0.410	0.399	0.188	0.125
BCSD	11.18	4.54	$+$ 0.12	0.477	1.05	0.446	0.386	0.215	0.141
DeepESD	8.88	3.55	$-$ 1.07	0.581	0.67	0.464	0.416	0.223	0.143

Table 1: Scores on the test seasons (2021–2024, all pixel-days of the study domain), without post-processing (top) and after quantile mapping (bottom). RMSE, MAE and bias in mm d -1 . The best value of each column within each block is in bold; bias and P99 ratio have no best value.

Figure 2: Daily precipitation on three test days at the 50th, 90th and 99th percentiles of the domain-mean observed precipitation (4.1, 6.4 and 8.2 mm d -1 ): IMERG, ERA5 precipitation, DeepESD, the U-Net, the U-Net after quantile mapping, and one member of the residual Wasserstein GAN. Values below 1 mm d -1 are white. The number in each panel is the spatial correlation with IMERG on that day.

Figure 3: Left: mean bias over the test seasons (mm d -1 ; brown too dry, green too wet), with its domain mean. Right: daily correlation with IMERG at each pixel, with its domain mean. Rows: ERA5 precipitation, BCSD, DeepESD, the U-Net without and with quantile mapping, and one member of the residual Wasserstein GAN; the U-Net with ERA5 precipitation is not shown. The dashed lines separate the three zones.

Figure 4:

\mathrm{FSS}_{20}

(a, b) and

\mathrm{CSI}_{20}

(c, d) on the test seasons, by zone and over land (a, c) and by month (b, d). Bars (a, c) and solid lines (b, d) show the scores after quantile mapping (QM); black ticks (a, c) and dotted lines (b, d) show the raw scores. The GAN is represented by one member.

Figure 5: Radially averaged power spectral density of the daily fields over the test seasons, for IMERG and the models, without post-processing (a) and after quantile mapping (b). Spectra are computed with pysteps on two

190\times 190

windows, each with its mean removed and a Hann taper, and averaged over the windows and the days. The curve of the GAN is that of one member.

	CRPS	SSR	SSR 2°	Outside (%)	RMSE mean	$\mathrm{FSS}_{20}$ mean
GAN	2.56	0.92	0.76	27	8.99	0.494
U-Net $+$ ERA5 precip.	3.41	—	—	—	8.58	0.516
	s/e 300 km	s/e 100 km	s/e 50 km	$P$ 100 km	$P$ 50 km	Areas (size)
GAN	0.96	1.03	1.15	1.09	1.37	96 (3)
U-Net $+$ ERA5 precip.	—	—	—	$<$ 0.01	$<$ 0.01	2 (514)
IMERG	—	—	—	1	1	101 (3)

Table 2: The ensemble of the GAN on the test seasons (ten members). Top: CRPS (mm d -1 ), spread-skill ratio (SSR) at the pixel scale and for 2° boxes, percentage of the pixel-days with IMERG outside the range of the members, among those on which IMERG or a member has at least 1 mm d -1 (18% for a calibrated ensemble), and RMSE (mm d -1 ) and

\mathrm{FSS}_{20}

of the ensemble mean; for the U-Net with ERA5 precipitation, on which the GAN builds, the CRPS is the MAE and the ensemble mean is the prediction. Bottom: ratio of the power of the differences between members to that of the errors (s/e) at 300, 100 and 50 km, ratio of the power of one member to that of IMERG (

P

) at 100 and 50 km, and number per day and median size (cells) of the areas with at least 20 mm d -1 .

Figure 6: Top: the test day at the 99th percentile of domain-mean precipitation (23 August 2024): IMERG, the prediction of the U-Net with ERA5 precipitation, on which the GAN builds, two members of the GAN, and the mean and the standard deviation of its ten members. (a) Rank of IMERG among the ten members over all test pixel-days (grey) and over the pixel-days on which IMERG or at least one member has at least 1 mm d -1 (red); amounts below 0.1 mm d -1 count as zero and ties are broken at random; the dashed line is the flat histogram of a calibrated ensemble. (b) Power spectra, computed as in Figure 5 , of the difference between a member and IMERG (error, five members) and between two members (spread, five pairs), averaged over the test days; for a calibrated ensemble the two curves coincide.

Figure 7: Increase in the RMSE of the U-Net on the test seasons when one input channel is permuted between days, for the whole domain (bars, with the standard deviation over three permutations) and for each zone (symbols). The RMSE without permutation is 8.61 mm d -1 .

Appendix figures & tables4 assets

Supplementary material from the paper’s appendix.

Appendix

Upper tail	Highest level	Pooling	RMSE	Bias	$r$	P99	$\mathrm{FSS}_{20}$	$\mathrm{CSI}_{20}$	$\mathrm{CSI}_{40}$
No mapping			10.72	$-$ 0.35	0.449	0.81	0.370	0.162	0.110
Ratio	maximum	month, $5\times 5$	19.19	$+$ 1.38	0.317	1.53	0.386	0.175	0.105
Ratio	0.99	month, $5\times 5$	17.54	$+$ 1.11	0.331	1.39	0.387	0.176	0.106
Difference	maximum	month, $5\times 5$	13.97	$+$ 0.82	0.396	1.32	0.390	0.177	0.110
Difference	0.99	month, $5\times 5$	12.90	$+$ 0.55	0.413	1.18	0.393	0.179	0.115
Cap	maximum	month, $5\times 5$	12.87	$+$ 0.59	0.406	1.21	0.391	0.178	0.111

Appendix

Table 3: Two-fold cross-validation of quantile-mapping designs within the validation seasons, for the ERA5 precipitation baseline. “Ratio” and “difference” extend the correction above the highest fitted quantile with a constant ratio or difference; “cap” sets those values to the highest fitted observed quantile. The retained design is in bold.

Figure 8: Training (dashed) and validation (solid) loss of the U-Nets (a) and of DeepESD (b). Dots mark the retained epochs; epochs are counted from 1.

	RMSE	MAE	Bias	$r$	P99	$\mathrm{FSS}_{20}$	$\mathrm{CSI}_{5}$	$\mathrm{CSI}_{20}$	$\mathrm{CSI}_{40}$
					ratio
Climatology	9.94	4.87	$-$ 0.19	0.383	0.34	0.058	0.340	0.022	0.000
ERA5 precipitation	10.72	4.32	$-$ 0.35	0.449	0.81	0.370	0.396	0.162	0.110
BCSD	11.21	4.73	$+$ 0.23	0.459	1.01	0.417	0.378	0.194	0.126
DeepESD	8.92	3.66	$-$ 1.03	0.572	0.63	0.427	0.420	0.198	0.123
U-Net	8.67	3.51	$-$ 0.95	0.602	0.65	0.459	0.452	0.222	0.140

Appendix

Table 4: Scores on the validation seasons (2019–2020), without post-processing. Same layout as Table 1 .

	RTX 5090	RTX 2080 Ti	CPU, 8 threads	CPU, 1 thread
DeepESD	0.18 (0.05)	0.64 (0.39)	2.6	4.3
U-Net	2.8 (2.2)	9.7 (6.9)	263	913
U-Net $+$ ERA5 precip.	2.9 (2.2)	9.8 (6.9)	295	860
GAN, ten members	20.0 (11.3)	50.6 (34.3)	1200	3340

Appendix

Table 5: Time to downscale one day of the test seasons with the trained models (ms per day), one day at a time and, on the GPUs, in batches of 16 days (in brackets). The GAN generates ten members. CPU: Intel Core i5-14600KF, with eight threads and with one thread.

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