# Precipitation to Mean Temperature Relationship

Based on the data below, I am trying to see if there is any relationship between Precipitation and Mean Temperature. Now the lm plot shows precipitation to increase with increasing temperature. However, the p-value is greater than 0.05 which tells me that statistically this could be by chance.

Is there any other method(s) which might be better suited to analyze such a relationship?

Data (df):

structure(list(year = 1980:2021, AvgTMean = c(24.2700686838937,
23.8852956598276, 25.094446596092, 24.1561175050287, 24.157183605977,
24.3047482638362, 24.7899738481466, 24.5756232655603, 24.5833086228592,
24.7344695534483, 25.3094451071121, 25.2100615173707, 24.3651692293534,
24.5423890611494, 25.2492166633908, 24.7005097837931, 24.2491591827443,
25.0912281781322, 25.0779264303305, 24.403294248319, 24.4983991453592,
24.4292324356466, 24.8179824927011, 24.7243948463075, 24.5086534543966,
24.2818632071983, 24.4567195220259, 24.8402224356034, 24.6574465515086,
24.5440715673563, 23.482670620977, 24.9979594684914, 24.5452453980747,
24.9271462811494, 24.7443215819253, 25.8929839790805, 25.1801908261063,
25.2079308058908, 25.0722425561207, 25.4554644289799, 25.4548979078736,
25.0756772250287), year.1 = 1980:2021, AvgPpt = c(202.704111130864,
114.247733460745, 126.199474853408, 141.152117838805, 116.203497094368,
130.77896716476, 126.350886628546, 107.205512960324, 91.6244152459231,
72.3675977029335, 107.065495697931, 138.900189676504, 114.878379703938,
117.930669633136, 156.362504334285, 149.231959978739, 123.880574706985,
148.447735131721, 135.237081261339, 145.989968136124, 105.967135316339,
120.919120551475, 105.667550237949, 119.329320418424, 94.5035688452337,
134.475944917217, 100.046199758505, 112.607532853243, 117.968374857272,
146.639791754533, 132.227290150763, 117.147468204471, 145.947365902934,
112.396272586057, 125.093465198045, 113.700379223659, 125.374770243706,
163.60349436601, 130.849234933763, 155.373781692023, 224.255287296813,
127.831766140872)), class = "data.frame", row.names = c(NA, -42L
))


Code:

# Static regression plot
df %>%
ggplot(aes(x = AvgTMean, y = AvgPpt)) +
geom_point(aes(color = "Precipitation"), size = 2, shape = 1, alpha = 0.5) +
geom_smooth(method = lm, aes(linetype = "LM"), se = FALSE, color = "red") +
scale_linetype_manual(values = 2, name = NULL) +
scale_colour_manual(values = "deepskyblue4", name = "Legend") +
theme(text = element_text(size = 16)) +
xlab("Temperature (\u00B0C)") +
ylab("Precipitation (mm)") +
ggtitle("Precipitation -  Temperature Relationship") +
guides(color = guide_legend(override.aes = list(alpha = 0.5), order = 1))

# LM Model
lm(AvgPpt ~ AvgTMean, data = df) -> model_df
get_formula(model_df)

summary(model_df)


Output:

• I think lm is probably right. But yes, probably best to go get advice from experts. Commented Sep 9, 2022 at 19:12
• @AllanCameron, for row 41 high precipitation value is due to hurricanes during that year. So the study area is prone to hurricanes. Commented Sep 9, 2022 at 19:15
• If hurricanes is an potentially important variable, maybe include it in the data frame? Please also include the plots in the post! Commented Sep 10, 2022 at 4:30
• @kjetilbhalvorsen, unfortunately I don't have the hurricane data, plus they are not a yearly occurrence in the study area. I have the plot though, thank you. Commented Sep 12, 2022 at 12:37