Comparison of Statistical Downscaling Methods for Monthly Total Precipitation: Case Study for the Paute River Basin in Southern Ecuador

Table 6

Statistical metrics for artificial intelligence and SDSM ensembles.

Station	Metric	AI	SDSM	AI_QM	SDSM_QM
El Labrado	Pearson correlation	0.58	0.37	0.58	0.38
	IRF	0.49	1.00	0.87	1.14
	Mean-bias	4.37	−41.49	−3.24	−0.54
	Cum_bias	38.23	126.89	12.19	9.96
	RMSE	37.70	65.41	41.67	51.44
Gualaceo	Pearson correlation	0.74	0.53	0.72	0.50
	IRF	0.52	0.46	1.04	1.03
	Mean-bias	−1.01	10.28	4.33	1.85
	Cum_bias	34.02	47.50	7.35	9.01
	RMSE	33.92	38.26	32.87	44.86
Paute	Pearson correlation	0.59	0.47	0.57	0.47
	IRF	0.36	0.47	0.80	0.89
	Mean-bias	−10.46	1.14	−4.26	1.34
	Cum_bias	47.61	31.73	15.59	7.77
	RMSE	30.60	30.26	31.13	35.03
Palmas	Pearson correlation	0.44	0.16	0.44	0.14
	IRF	0.52	0.54	1.12	1.02
	Mean-bias	7.39	16.31	0.97	−3.66
	Cum_bias	38.14	56.93	8.77	12.28
	RMSE	47.45	56.09	55.73	66.62
Biblián	Pearson correlation	0.66	0.46	0.67	0.45
	IRF	0.61	0.56	1.29	1.25
	Mean-bias	−11.12	−2.87	−1.21	1.66
	Cum_bias	35.01	29.99	18.91	17.30
	RMSE	41.73	44.89	39.87	51.81